All Countries Regional Data
AIM: Create region by region analysis of male vs female for each age groups (0-4, 5-14, 0-14 and 15 plus) for all countries separated by region
library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, unionlibrary(tidyverse)Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
── Attaching packages ──────────────────────────────────────────── tidyverse 1.3.1 ──
✓ ggplot2 3.3.5 ✓ purrr 0.3.4
✓ tibble 3.1.3 ✓ stringr 1.4.0
✓ tidyr 1.1.3 ✓ forcats 0.5.1
✓ readr 2.0.1
── Conflicts ─────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()library(tidyr)
library(forecast)Registered S3 method overwritten by 'quantmod':
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as.zoo.data.frame zoo library(fpp2)── Attaching packages ─────────────────────────────────────────────────── fpp2 2.4 ──
✓ fma 2.4 ✓ expsmooth 2.3library(stringr)
library(magrittr)
Attaching package: ‘magrittr’
The following object is masked from ‘package:purrr’:
set_names
The following object is masked from ‘package:tidyr’:
extractlibrary(ggplot2)Organising the data by g_whoregion
grouped_2013 <- group_by(world_2013, g_whoregion) %>% filter(year == 2013) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)),
newrel_f04 = sum(na.omit(newrel_f04)),
newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)),
newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)),
newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2013)
grouped_2014 <- group_by(world_2013, g_whoregion) %>% filter(year == 2014) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)),
newrel_f04 = sum(na.omit(newrel_f04)),
newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)),
newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)),
newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2014)
grouped_2015 <- group_by(world_2013, g_whoregion) %>% filter(year == 2015) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)),
newrel_f04 = sum(na.omit(newrel_f04)),
newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)),
newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)),
newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2015)
grouped_2016 <- group_by(world_2013, g_whoregion) %>% filter(year == 2016) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)),
newrel_f04 = sum(na.omit(newrel_f04)),
newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)),
newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)),
newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2016)
grouped_2017 <- group_by(world_2013, g_whoregion) %>% filter(year == 2017) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)),
newrel_f04 = sum(na.omit(newrel_f04)),
newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)),
newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)),
newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2017)
grouped_2018 <- group_by(world_2013, g_whoregion) %>% filter(year == 2018) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)),
newrel_f04 = sum(na.omit(newrel_f04)),
newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)),
newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)),
newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2018)
grouped_2019 <- group_by(world_2013, g_whoregion) %>% filter(year == 2019) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)),
newrel_f04 = sum(na.omit(newrel_f04)),
newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)),
newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)),
newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2019)
grouped_2020 <- group_by(world_2013, g_whoregion) %>% filter(year == 2020) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)),
newrel_f04 = sum(na.omit(newrel_f04)),
newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)),
newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)),
newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2020)
bind_world <- rbind(grouped_2013, grouped_2014, grouped_2015, grouped_2016, grouped_2017, grouped_2018, grouped_2019, grouped_2020)
bind_world <- bind_world[, c(1, 10, 2:9)]
arrange_world <- arrange(bind_world, g_whoregion)
##Including columns for total values
mutate_world <- mutate(arrange_world, "newrel_tot04" = newrel_m04 + newrel_f04, "newrel_tot514" = newrel_m514 + newrel_f514, "newrel_tot014" = newrel_m014 + newrel_f014, "newrel_tot15plus" = newrel_m15plus + newrel_f15plus)
perc_world <- mutate(mutate_world, "perc_014" = 100 * (newrel_tot014/(newrel_tot014 + newrel_tot15plus)), "perc_youngkids" = 100 * (newrel_tot04/newrel_tot014))
Creating graphs for male vs female for 0-4 by g_whoregion
afr_04 <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m04, newrel_f04) %>%
t()
colnames(afr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_04 <- afr_04[, -8]
afr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,30000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in AFR")
numeric(0)
amr_04 <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m04, newrel_f04) %>%
t()
colnames(amr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_04 <- amr_04[, -8]
amr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,3000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in AMR")numeric(0)
emr_04 <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m04, newrel_f04) %>%
t()
colnames(emr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_04 <- emr_04[, -8]
emr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,20000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in EMR")numeric(0)
eur_04 <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m04, newrel_f04) %>%
t()
colnames(eur_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_04 <- eur_04[, -8]
eur_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,3000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in EUR")numeric(0)
sea_04 <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m04, newrel_f04) %>%
t()
colnames(sea_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_04 <- sea_04[, -8]
sea_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,60000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in SEA")numeric(0)
wpr_04 <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m04, newrel_f04) %>%
t()
colnames(wpr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_04 <- wpr_04[, -8]
wpr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,20000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in WPR")numeric(0)
Creating graphs for notifications in 5-14 years by g_whoregion
afr_514 <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m514, newrel_f514) %>%
t()
colnames(afr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_514 <- afr_514[, -8]
afr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,40000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-514 years in AFR")numeric(0)
amr_514 <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m514, newrel_f514) %>%
t()
colnames(amr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_514 <- amr_514[, -8]
amr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,4000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in AMR")numeric(0)
emr_514 <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m514, newrel_f514) %>%
t()
colnames(emr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_514 <- emr_514[, -8]
emr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,30000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in EMR")numeric(0)
eur_514 <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m514, newrel_f514) %>%
t()
colnames(eur_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_514 <- eur_514[, -8]
eur_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,5000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in EUR")numeric(0)
sea_514 <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m514, newrel_f514) %>%
t()
colnames(sea_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_514 <- sea_514[, -8]
sea_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,100000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in SEA")numeric(0)
wpr_514 <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m514, newrel_f514) %>%
t()
colnames(wpr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_514 <- wpr_514[, -8]
wpr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,30000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in WPR")numeric(0)
Creating plot for 0-14
```r
afr_014 <- perc_world %>% filter(g_whoregion == \AFR\) %>% select(newrel_m014, newrel_f014) %>%
t()
colnames(afr_014) <- c(\2013\, \2014\, \2015\, \2016\, \2017\, \2018\, \2019\, \2020\)
afr_014 %<>% barplot(col = c(\skyblue\, \seagreen1\), beside = TRUE, ylim = c(0,70000), xlab = \year\, ylab = \notifications\) + title(\Sex disaggregated notifications for ages 0-14 years in AFR\)
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numeric(0)
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" />
<!-- rnb-plot-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuYW1yXzAxNCA8LSBwZXJjX3dvcmxkICU+JSBmaWx0ZXIoZ193aG9yZWdpb24gPT0gXFxBTVJcXCkgJT4lIHNlbGVjdChuZXdyZWxfbTAxNCwgbmV3cmVsX2YwMTQpICU+JSBcbiAgdCgpXG5jb2xuYW1lcyhhbXJfMDE0KSA8LSBjKFxcMjAxM1xcLCBcXDIwMTRcXCwgXFwyMDE1XFwsIFxcMjAxNlxcLCBcXDIwMTdcXCwgXFwyMDE4XFwsIFxcMjAxOVxcLCBcXDIwMjBcXClcbmFtcl8wMTQgJTw+JSBiYXJwbG90KGNvbCA9IGMoXFxza3libHVlXFwsIFxcc2VhZ3JlZW4xXFwpLCBiZXNpZGUgPSBUUlVFLCB5bGltID0gYygwLDYwMDApLCB4bGFiID0gXFx5ZWFyXFwsIHlsYWIgPSBcXG5vdGlmaWNhdGlvbnNcXCkgKyB0aXRsZShcXFNleCBkaXNhZ2dyZWdhdGVkIG5vdGlmaWNhdGlvbnMgZm9yIGFnZXMgMC0xNCB5ZWFycyBpbiBBTVJcXClcbmBgYFxuYGBgIn0= -->
```r
```r
amr_014 <- perc_world %>% filter(g_whoregion == \AMR\) %>% select(newrel_m014, newrel_f014) %>%
t()
colnames(amr_014) <- c(\2013\, \2014\, \2015\, \2016\, \2017\, \2018\, \2019\, \2020\)
amr_014 %<>% barplot(col = c(\skyblue\, \seagreen1\), beside = TRUE, ylim = c(0,6000), xlab = \year\, ylab = \notifications\) + title(\Sex disaggregated notifications for ages 0-14 years in AMR\)
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVtZXJpYygwKVxuIn0= -->
numeric(0)
<!-- rnb-output-end -->
<!-- rnb-plot-begin -->
<img 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" />
<!-- rnb-plot-end -->
<!-- rnb-source-begin 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 -->
```r
```r
emr_014 <- perc_world %>% filter(g_whoregion == \EMR\) %>% select(newrel_m014, newrel_f014) %>%
t()
colnames(emr_014) <- c(\2013\, \2014\, \2015\, \2016\, \2017\, \2018\, \2019\, \2020\)
emr_014 %<>% barplot(col = c(\skyblue\, \seagreen1\), beside = TRUE, ylim = c(0,50000), xlab = \year\, ylab = \notifications\) + title(\Sex disaggregated notifications for ages 0-14 years in EMR\)
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVtZXJpYygwKVxuIn0= -->
numeric(0)
<!-- rnb-output-end -->
<!-- rnb-plot-begin -->
<img 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" />
<!-- rnb-plot-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuZXVyXzAxNCA8LSBwZXJjX3dvcmxkICU+JSBmaWx0ZXIoZ193aG9yZWdpb24gPT0gXFxFVVJcXCkgJT4lIHNlbGVjdChuZXdyZWxfbTAxNCwgbmV3cmVsX2YwMTQpICU+JSBcbiAgdCgpIFxuY29sbmFtZXMoZXVyXzAxNCkgPC0gYyhcXDIwMTNcXCwgXFwyMDE0XFwsIFxcMjAxNVxcLCBcXDIwMTZcXCwgXFwyMDE3XFwsIFxcMjAxOFxcLCBcXDIwMTlcXCwgXFwyMDIwXFwpXG5ldXJfMDE0ICU8PiUgYmFycGxvdChjb2wgPSBjKFxcc2t5Ymx1ZVxcLCBcXHNlYWdyZWVuMVxcKSwgYmVzaWRlID0gVFJVRSwgeWxpbSA9IGMoMCw4MDAwKSwgeGxhYiA9IFxceWVhclxcLCB5bGFiID0gXFxub3RpZmljYXRpb25zXFwpICsgdGl0bGUoXFxTZXggZGlzYWdncmVnYXRlZCBub3RpZmljYXRpb25zIGZvciBhZ2VzIDAtMTQgeWVhcnMgaW4gRVVSXFwpXG5gYGBcbmBgYCJ9 -->
```r
```r
eur_014 <- perc_world %>% filter(g_whoregion == \EUR\) %>% select(newrel_m014, newrel_f014) %>%
t()
colnames(eur_014) <- c(\2013\, \2014\, \2015\, \2016\, \2017\, \2018\, \2019\, \2020\)
eur_014 %<>% barplot(col = c(\skyblue\, \seagreen1\), beside = TRUE, ylim = c(0,8000), xlab = \year\, ylab = \notifications\) + title(\Sex disaggregated notifications for ages 0-14 years in EUR\)
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVtZXJpYygwKVxuIn0= -->
numeric(0)
<!-- rnb-output-end -->
<!-- rnb-plot-begin -->
<img 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Dz66KOinfXU9qFNmzZyww03SIMGnLpr+74ifwgggAACCCCAAAIIIIBAbRTg12Rt3CvkCQEESiYwbtw423t3yVZY5IrOOecc6datW5GpsDgCCCCAAAIIIIAAAggggEAlChAIrMS9zjbXaYHdO7bb/F999dXStGnTWr0tHTp0kCuvvLJW5/Gzzz6z+es19OfStFWbWpvX2b+/QTate1/8/NbajJIxBBBAAAEEEEAAAQQQQACBWitAILDW7hoyhoBbYMfmjXbCQw895J6hlo0dPHiwdOzYsZblKj07HXqeLJ//Yqf0CbVkzF8emSib5P1akpu6nw2tCv7BBx/UiSrh2nlUbQ/61/0jgi1AAAEEEEAAAQQQQKAyBAgEVsZ+ZivLSMCTf7dld8S4fvK5g5rU2i1b9qsXZNf6T2XPnj21No9krHIFhg0bJnfddVedAGjYsKG89dZbcuSRR9aJ/JJJBBBAAAEEEEAAAQQQqL0CBAJr774hZwhkFGhzTnfZt0OLjPPU5MT3bpllA4E1mQfWjUCYwLJly+ykqmbNZZ+GnwubrcbHb9/0iezevUtWr15NIDCCvTF79my59dZb60QVey0FetNNN8lBBx0UwZaTBAIIIIAAAggggAAC/xYgEMiRgAACCCBQsQIDr5ko/9P9hFq7/Y9e8T1Z9frMWpu/upYxLQX6+OOP15ls9+vXT773ve/VmfySUQQQQAABBBBAAIHaL0AgsPbvI3KIAAIIIIAAAhEI+J3tfPnsH0ubzj0iSDGeJN6Y/n9SvXi+7N27N54VkCoCCCCAAAIIIIBAxQoQCKzYXc+GI4AAAgggUJkCrY/qKod9vV+t3fhlM5+ttXkjYwgggAACCCCAAAJ1W6B+3c4+uUcAAQQQQAABBBBAAAEEEEAAAQQQQACBXARiKRHoeZ7tKVR7Okwe3nnnHXn++edl5syZcuCBB8q3v/1tGTRokNSrVy95Nt4jgAACCCCAAAIIIIAAAggggAACCCCAQMQCkQYC161bJ5MmTZIpU6bY1759+yay+9JLL4k2eu23z6MTHnjgATn//PPl/vvvT8zHGwQQQAABBBBAAAEEEEAAAQQQQAABBBCIXiCyQODOnTttoG/JkiU2l++++24it9XV1TJkyJCUIKA/UYOGXbp0kZEjR/qjeEUAAQQQQAABBBBAAAEEEEAAAQQQQACBiAUiayNw7Nix4gcBtapv/fr/TXrixIny8ccf26x37dpVXnvtNdESgscee6wdd+WVV8rf/va3iDeN5BBAAAEEEEAAAQQQQAABBBBAAAEEEEDAF4ikRKBW973jjjtsmhrcu/vuu6Vbt27+OuSRRx5JvL/33nsT0zQY2L59e9m8ebMNDh599NGJ+XiDAAIIIIAAAggggAACCCCAAAIIIIAAAtEJ/LfYXhFpajVgrRqsw1VXXZUI9OnnZcuWyXvvvadv5YgjjkiZph2GnHnmmXbam2++aV/5hwACCCCAAAIIIIAAAggggAACCCCAAALRC0QSCHz77bdtzvbZZx/p06dPSi5nzJiR+Hzqqacm3vtvtESgDosWLbKv/EMAAQQQQAABBBBAAAEEEEAAAQQQQACB6AUiCQS+//77Nmdt2rSRAw44ICWXyYHAb37zmynT9MPu3bvtuD179qRNYwQCCCCAAAIIIIAAAggggAACCCCAAAIIRCMQSSDw0EMPtbn517/+lZKr7du3y+zZs+24xo0bS69evVKm64e///3vdly7du3SpjECAQQQQAABBBBAAAEEEEAAAQQQQAABBKIRiCQQ+MUvftHmZseOHbY3YD9r06ZNEx2nw8knnyxVVVX+JPu6atUqefLJJ+37Dh06pEzjAwIIIIAAAggggAACCCCAAAIIIIAAAghEJxBJILBz585y1FFH2VxdfPHF8vTTT8tDDz0kI0eOTOT0vPPOS7zXN/PmzZNTTjklUTX4ggsuSJnOBwQQQAABBBBAAAEEEEAAAQQQQAABBBCITqBBFEnVq1dP/vd//1fOPvts0fYCBw0alJLsiSeeKIMHD06MO+mkk2TWrFmJzwMHDpSuXbsmPvMGAQQQQAABBBBAAAEEEEAAAQQQQAABBKIViKREoGZpyJAhct9990nDhg1TcqglBbX67+c+97nE+GbNmiXeDxgwQB588MHEZ94ggAACCCCAAAIIIIAAAggggAACCCCAQPQCkZQI9LOl1Xv79u1rS/utXr3atgvYo0cPqV8/Nd6opf88z5Pvfve7thRhcLqfHq8IIIAAAggggAACCCCAAAIIIIAAAgggEI1ApIFAzdLBBx9sSwdmyt4111yTaTLTEEAAAQQQQAABBBBAAAEEEEAAAQQQQCBigcgDgRHnj+QQQAABBBBAAAEEEChKYNu2bbJnz56i0ijFwtqUTuPGjUuxKtaBAAIIIIAAAhUqQCCwQnc8m40AAggggAACCFSCwKRJk2To0KF1YlO1uZw//vGPcsopp9SJ/JJJBBBAAAEEEKh7ApEHAj/99FN59NFHZdmyZbJ161b79FXbA8w2aKch+vf/2bsbOC2rOnH4P15GGERwQQRUCCRZxUCUjMo1cgljBbEyzdUnk0fX2AVTRAzYv/1rXchMSysLK6UlqUdLw9RYt7RkU5DNIMGXwAQcJSHlVWCAEZ49lzv3KjIv98w9433PfM/nczHXy7nO+V3fCwp+nnMdhQABAgQIECBAgEChBP7whz9kTbU7qEO0a//WRe0K1Uch2qnavSv2Vu2JFStWSAQWAlQbBAgQIECAwAEFCpoIvP7662PmzJmxdevWA3ZW28levXpJBNYG5BoBAgQIECBAgECDBf520hfjxI9/psH3N/WND33j/8YTP/leU3ejfQIECBAgQKCVCxQsETh//vz4/Oc/38o5PT4BAgQIECBAgAABAgQIECBAgACB4hQoSCJw7969cfHFF+ee8KSTTsq+xdK/f/84+OCDo02bNrlrNe0cddRRNV1yngABAgQIECBAgAABAgQIECBAgACBRgoUJBGYvge4cePGLJSPfvSjcc8990SnTp0aGZrbCRAgQIBA6xbYuXVzBvDVr3415s2bV9QY3bp1i1mzZmX/AbCoAxUcAQIECBAgQIAAgVYsUJBE4O9///sc4ZQpUyQBcxp2CBAgQIBAwwW2/Lkiu/nhhx9ueCPNeOfpp58eY8aMacYedUWAAAECBAgQIECAQD4CBUkEVlVVZX2mKcCnnHJKPv2rS4AAAQIECNQksG9fduXoqadFlyG9a6r1jp9//uuPxNbfvxTVfx94xwMSAAECBAgQIECAAAECBxQoSCLwAx/4QNb4vv/+B0uaImxa8AGtnSRAgAABAg0SOOxv3x2Hjz6uQfc2x01//ukfskRgc/SlDwIECBAgQIAAAQIEGi7QtuG3/u+dAwcOjO7du2cnHnroof+9YI8AAQIECBAgQIAAAQIECBAgQIAAgaIQKEgiMD3JNddckz3QjBkz4pVXXimKhxMEAQIECBAgQIAAAQIECBAgQIAAAQJvCBQsEXj55ZdHSgKuW7cujj322Pj2t78dzz77bOzcuZM1AQIECBAgQIAAAQIECBAgQIAAAQLvsEBBvhG4ZcuWmDhxYvYo5eXl8eqrr+aO08lDDz002rVrV+ujXn311ZE2hQABAgTeLrB75/bs5M033xy9evV6e4UiOpPimzx5crRtW7D/1lRETycUAs0nsHnz5nj55Zebr8MG9JS+C92lS5cG3OkWAgQIECBAgACBd0KgIInAysrKmDdvXo3xp7/I1lWMHKxLyHUCBFqzwLYNf84e/4477igJhg996ENx8sknl0SsgiRQbAJ/ef7ZLKTLLrss0lbMpW27tvGbX/8mTj311GIOU2wECBAgQIAAAQL/I1CQRGAa9XHUUUc1CtV/TW4Un5sJEGjhAmlV9lT6fe7UKO9zaNE+7Zpbfhs712yK3bt3F22MAiNQ7AK7t2/LQmzftWO0Ky8r2nD3bKmMvTv3xMqVKyUCi/YtCYwAAQIECBAg8FaBgiQCe/ToERUVFW9t2REBAgQIFFzgqE+/Nw59b5+Ct1uoBl+evzxLBBaqPe0QaM0C7/nWJ+Ko/+e9RUvwh0vujIrbHi/a+ARGgAABAgQIECDwdgEfcHq7iTMECBAgQIAAAQIECBAgQIAAAQIEWpyARGCLe6UeiAABAgQIECBAgAABAgQIECBAgMDbBQoyNfjtzUakBUKefvrp+OMf/xjPPvts7Nq1K9IU4p49e8aIESPimGOOOdBtzhEgQIAAAQIECBAgQIAAAQIECBAg0AQCBU8EpoTf9ddfH7NmzYq0mnBNZciQIVmdMWPG1FTFeQIECBAgQIAAAQIECBAgQIAAAQIECiRQ0KnBy5cvj8GDB8cXvvCFWpOAKfYnn3wyxo4dG9OnTy/Qo2iGAAECBAgQIECAAAECBAgQIECAAIGaBAo2IjCNBPz7v//7WLVqVdZXWVlZfPKTn4zjjjsu+vfvHx07doy1a9dm27333hsvvPBCVu+6667L6lx44YU1xeg8AQIECBAgQIAAgRYtsG/fvuz5Kioq4ve//31RP2t5eXn29/eiDlJwBAgQIECAwAEFCpYITKMAn3rqqayTNNLvpptuigEDBhyw069+9avx3e9+N66++ups5OCkSZPiE5/4RHTu3PmA9Z0kQIAAAQIECBAg0JIFNqxcnj3e1772tUhbsZd58+bF+eefX+xhio8AAQIECBDYT6AgicCqqqq4+eabs6bf+973xt133x0HHXTQfl3972GHDh3isssui/Tzs5/9bGzbti1+/OMfxz/8wz/8byV7BAgQIECAAAECBFqJwO6d27Mn7XhU1zjo8OL9j+OVL22J3etfixdffLGVvBmPSYAAAQIEWpZAQRKBaWXgNDU4lW9+85u1JgHfzHfppZdmIwOfeOKJePDBByUC34xjnwABAgQIECBAoNUJHD3lw3H0FSOK9rmf+fx98afrf1208QmMAAECBAgQqF2gIInAtPBHKmmE30knnVR7j/td/eAHPxgpEZi+H6gQIECAAAECBAgQIECgsQJLly6NyZMn5wYrNLa9prw//RsqTQfP999RTRmTtgkQIECg5QoUJBG4ZcuWTKhTp071Hg1YTdq1a9dsN00vVggQIECAAAECBAgQINBYgfnz58cjjzzS2Gaa7f4Ur0Rgs3HriAABAq1aoCCJwGOPPTZD3LRpUzz//PNx9NFH1xs1jQZMZfDgwfW+R0UCBAgQIECAAAECBAjUJFC9CvOQMy+IIWPOq6naO35++S/ujD/8/I7Yu3fvOx6LAAgQIECgdQgUJBE4aNCgnFZaNKR64ZDcyRp20pTiX//6jW+MSATWgOQ0AQIECBAgQIAAAQINEuhy+BFxxPHDGnRvc9y09ne/bY5u9EGAAAECBHICbXN7jdg5/PDDY/To0VkL3/jGN+K2226rs7UXXnghPvnJT0ZlZWW0b98+d3+dN6pAgAABAgQIECBAgAABAgQIECBAgEDeAgUZEZh6vemmm7LpvXv27IlLLrkkvve978XUqVPjuOOOi379+kXHjh3jpZdeijVr1sRPf/rTmD17duzevTsLePr06aYG5/3q3ECAAAECBAgQIECAAAEC1QLpu/M///nPY8eOHdWnivZnGgwzZsyYOOSQQ4o2RoERINAyBQqWCPzrv/7rSKMBr7jiimx1rscffzwb8VfNlv6H7kALgpx88slxzTXXVFfzkwABAgQIECBAgAABAgQI5C1wxx13xPjx4/O+75264eqrr46vfOUr71T3+iVAoJUKFCwRmPwmTJgQp556anz605+OpUuXvoV0/yRg586dswRgShyWlZW9pa4DAgQIECBAgAABAgQIECCQj8DWrVuz6t36vjt6HTskn1ubte7Giufj5WeWRXW8zdq5zggQaPUCBU0EJs3jjz8+0mjAn/3sZ/HUU0/FM888k22bN2+OAQMGxDHHHBMDBw6MCy64II444ohW/wLWrVsXGzduzIavpyHsaQp1165do0uXLtG9e/fsuNUjASBAgAABAgQIECBAgEA9Bfq9b0R85PJr61m7+astnT83SwQ2f896JECAQETBE4EJNY3wO/fcc/keQGDbtm0xd+7cmDdvXqxYsSLScU0lTadOqykPHz48xo4dG2eccUa0adOmpurOEyBAgAABAgQIECBAgAABAgQIEKhRoCCrBtfYugs5gfXr18fEiRPjyCOPjEmTJsWiRYtqTQKmG9N06jTFOi2skhKBQ4YMiQceeCDXph0CBAgQIECAAAECBAgQIECAAAEC9RVokhGB9e28tdTbtGlTjBo1KpYvX5575DSyr3fv3tG3b9/o0aNHlJeXR4cOHbLkX2VlZfa9iIqKili7dm22+Eq6MY0gHDduXNx4443Zoiy5xuwQIECAAAECBAgQIECAAAECBAgQqEMgr0TgnDlzYvLkyVmTgwYNisceeyzb37BhQ/bdvzr6qvXytGnTIm0trWzfvj1bFr46CZhWSb7yyitj5MiRWQKwrufds2dPLFmyJJtOnPzTcXoH6TuLaaqwQoAAAQIECBAgQIAAAQIECBAgQKA+AnklAnfv3h1btmzJ2n3zCkf79u3Lna9Ppweqs2vXrgOdLvlzd911VzYNOD3Ieeedl30bsG3b+s/ITt9bPOWUU7LtrLPOio997GNZMjAlTUePHh35tFXymB6AAAECBAgQIECAAAECBAgQIECgwQJ5JQIPOeSQ6NevX9ZZ+tZddWnXrl3ufPW5fH8eeuih+d5SEvWrR02m7/ulRUIak7hLIwBvuOGGuPzyy7NpxqtXr85WYi4JCEESIECAAAECBAgQIECAAAECBAi8owJ5JQLPP//8SNv+5bDDDouUlFLeLvDoo49mJ88888xsNeW318jvzNlnn50lAtNdK1eulAjMj09tAgQIECBAgAABAgQIECBAgECrFaj/HNVWS9S4B3/xxRezBvr06dO4hv7n7u7du+cSijt37ixImxohQIAAAQIECBAgQIAAAQIECBBo+QIFSQSmBSx+85vfZFv6jmA+5Sc/+Ul86Utfivnz5+dzW8nUHTBgQBbrokWLChJzmmqcvFM58cQTC9KmRggQIECAAAECBAgQIECAAAECBFq+QEESgRs3bozTTjst29J+PuXiiy+OL37xi/GLX/win9tKpu6wYcOyWO+888545JFHGhX35s2bY8qUKVkb3bp1i/79+zeqPTcTIECAAAECBAgQIECAAAECBAi0HoG8vhFYaJY0tbV6euurr75a6OaLor3p06dni4RUVlZGWvX3K1/5SowfPz4OOuigvOJbtmxZXHrppZF+pjJhwoS87leZAAECBAgQIECAAIHiEti9c3sW0H/8x3/Ea6+9VlzB7RdN+vfL5z73uTjqqKP2u+KQAAECBEpJIO9E4D333BPr1q17yzNu27YtdzxnzpxIqwvXVXbt2hULFiyIqqqqrOrxxx9f1y0leT1NDZ41a1ZMnTo1tmzZkiXw0v6IESNi6NCh2ai+nj17Rnl5eXTs2DHzSEnDrVu3RkVFRTz33HOxcOHCWLFiRe75Tz/99Lj22mtzx3YIECBAgAABAgQIECg9gfUrl2dB/9d//VekrdhLWVlZzJw5s9jDFB8BAgQI1CKQdyLw9ddfj8suu6zGJmfMmFHjtdouDB8+vLbLJX3tqquuirTIx8SJE7MRkClxev/992dbvg82evTomDdvXrRtW5BZ3fl2rz4BAgQIECBAgAABAgUS2Ld3b9bSYaMGxuFjBhWo1cI38+rDq2L9z5/Kfau88D1okQABAgSaSyDvROA555wTo0aNil/+8pcFi3HatGkxZsyYgrVXjA2l6cBjx46Nm266KW6//fZ4+eWX6x1mhw4dIiUAL7nkkqyNet+oIgECBAgQIECAAAECRS9w6Pv6xtGXf6ho49y3uypLBBZtgAIjQIAAgXoL5J0ITC1///vfj4cffjjXSZrGevnll2fHKdHVtWvX3LUD7bRp0yabBtu5c+dIU4L79et3oGot7lyPHj2yofRpOP2aNWti8eLFsWrVqmwacJo2nEYKpuH2yaVLly6RphUPGjQoTjjhhOxciwPxQAQIECBAgAABAgQIECBAgAABAs0m0KBEYN++feOiiy7KBbl+/fpcIvBTn/pU9OrVK3fNzoEFUvKztSRADyzgLAECBAgQIECAAAECBAgQIECAQHMKNCgRuH+AaXGQNBIwlTSSTSFAgAABAgQIECBAgAABAgRqFtizZ0/Rrxbdrl27bGHLmp/CFQIESk2gIInATp065UYElhrAOx1vWoF548aNsWPHjmxLKwenqdUpoZoWGEnHCgECBAgQIECAAAECBAi0DIFNL67OHuS2226LtBV7ufnmm+Nzn/tcsYcpPgIE6ilQkERgPfuqsVpaOOOVV16J97znPTXWaSkX0ncA586dm638u2LFiuy7gDU9W/v27WPw4MGRVlROC42cccYZkb6vqBAgQIAAAQIECBAgQIBAaQq89ur6LPA2ZW2jbYei+Cf5ASH37Xk99u56PZYtW3bA604SIFCaAgX/X52XXnop7r777tiwYUPs3r079u7d+xaZ119/PdJWVVUVmzdvjhdffDEee+yx+D//5/+06ERg+o7iv/zLv8QPf/jDWpN/b8ZKRkuXLs222bNnZz7XXXddi19h+c0G9gkQIECAAAECBAgQINASBfpcPDyGfOecon20ijmPxx/+3zuLNj6BESDQMIGCJgInTZoU3/3udyN960D5X4FNmzbFqFGjYvny5bmTaWRf7969Iy28klYTLi8vjw4dOmQJ0srKymwl4YqKili7dm3s2rUruy+NIBw3blzceOONccUVV+TaskOAAAECBAgQIECAAAECBAgQIECgLoGCJQL/7d/+LW655Za6+nvb9fTx0fe9733x/ve//23XWsKJ7du3ZyP4qpOAJ598clx55ZUxcuTILAFY1zOmpOqSJUuy6cRz5szJkqyTJ0+OgQMHZlOF67rfdQIECBAgQIAAAQIECBAgQIAAAQJJoCCJwDT9NyW3qsu5554bo0ePjp49e8Y555yTLYLxpS99KfveXVoYIyW20hTZnTt3xmmnnRa//OUvq29tcT/vuuuuWLRoUfZc5513XvZtwLZt29b7OcvKyuKUU07JtrPOOis+9rGPZcnAadOmZcb5tFXvTlUkQIAAAQIECBAgQIAAAQIECBBocQL1z0jV8uhpCmtK8KUyYcKEuPPOO2P8+PHZiLW/+Zu/yc6nkXEf//jH4+KLL45bb701FixYEJ07d45f/epX8eMf/zir0xJ/Sd8/TGXIkCHZqL7GJO7SYiE33HBD1l4aYbh69RurTWUn/EKAAAECBAgQIECAAAECBAgQIECgFoGCJAJXrlyZ6yKNVHtzSaPZUnnooYfefDpGjBiRjQRM38pLU13TwiEtsTz66KPZY5155pmRRvc1tpx99tm5Jt7snjtphwABAgQIECBAgAABAgQIECBAgMABBAqSCHzuueeypjt16hTvete73tLNcccdlx0/9dRT2WrBb76Yvgs4ePDgSCvqplGELbGkVZFT6dOnT0Eer3v37rmEYpparRAgQIAAAQIECBAgQIAAAQIECBCoj0BBEoEpAZjKX/3VX72tz7SoRSppJdwDjWBLIwNTqV5MIztoQb8MGDAge5rq7wQ29tHSVOPqVZlPPPHExjbnfgIECBAgQIAAAQIECBAgQIAAgVYiUJBE4LHHHptxbdiwIfbt2/cWumOOOSbS9N9U/vCHP7zlWjpIIwJTaamJwGHDhmXPl0Y8PvLII9l+Q39J06enTJmS3d6tW7fo379/Q5tyHwECBAgQIECAAAECBAgQIECAQCsTKGgiMI1UW7hw4VsI02jBI444Ijv3u9/97i3X0sFvf/vb7Fz1YiNvq1DiJ6ZPn55N5U0jItOqv2mhlN27d+f9VMuWLYvTTz890s9U0qIsCgECBAgQIECAAAECBAgQIECAAIH6CrSvb8Xa6nXt2jX69esXa9asiSuuuCJ+8YtfRO/evXO3pCmsL730UvYdwH/+53/OTSHeu3dvVjdVPProo3P1W9JOmho8a9asmDp1amzZsiVL4KX9NCV66NCh2ai+nj17Rnl5eXTs2DGqqqqyadRbt26NtBpz+v5iSq6uWLEix5ISgtdee23u2A4BAgQIECBAgAABAgQIECBAgACBugQKkghMndx8883ZiLc0Yi0tEHLBBRfELbfckvV/0UUXxf333x9p4YyxY8dmSbFDHd8FLAAAQABJREFUDz00u+eVV17J6lRPEc4OWtgvV111VaRFPiZOnBhpgY9t27ZlHskk3zJ69OiYN29etG1bkMGc+XavPgECBAgQIECAAAECBAgQIECAQIkKFCybNG7cuLjwwgszhjTy7d57782RfPzjH4/0rcBU0mIX6fi0006L+fPnZ+e6dOkSn/vc57L9lvrL+PHjY+3atTFjxozo1atXXo/ZoUOHLMl63333xYIFCyJ9H1AhQIAAAQIECBAgQIAAAQIECBAgkI9AwUYEpk6/853vRFoc45vf/OZbpgan0WsPPvhgpNFs+68cnKbEpu/mHX744fnEXZJ1e/ToETNnzsy2NI168eLFsWrVqkjTgFPyNI0ULCsri86dO0dKjqZpxYMGDYoTTjghO/dOPfQdd9yRvaP9F4JpSDzPP/98dttf/vKXhtzuHgIECBAgQIAAAQIECBAgQIAAgQYKFDQRmBYGSSP7LrvssizB9eaY0gq3aTTgD3/4w/jNb34TaUpw+nbgP/7jP2bJrjfXbQ376ZuKaSuFkqYwVy/qUqh40zRxhQABAgQIECBAgAABAgQIECBAoPkECpoIrA67TZs2MXDgwOrD3M/0nby0mEjalNIRuP322+Pyyy+PtLhLY8s3vvGNuOuuu7IkcGPbcj8BAgQIECBAgAABAgQIECBAgED9BZokEZgWxFiyZEm2Mu7+oSxfvjxbKCN9J/DYY4/d/3KrO163bl1s3LgxduzYkW1p5eC0CnOaGpwSp+n4nS5ppOcHPvCBgoTx05/+tCDtaIQAAQIECBAgQIAAAQIECBAgQCA/gYImAquqqmLatGnx3e9+N/vW3auvvvq2aJ544olswYy0aMbIkSPjRz/6Uav4PmA1RPoO4Ny5c7OVf1esWJF9F7D62v4/27dvH2k15eHDh2erLZ9xxhmRRlsqBAgQIECAAAECBAgQIECAAAECBPIVKNiqwWlEW0pU3XjjjVlyK41yS98B3L+sXr06d+qhhx7KFhd5+umnc+da6s769etj4sSJceSRR8akSZNi0aJFtSYBk0NKrC5dujRmz56dJQKHDBkSDzzwQEsl8lwECBAgQIAAAQIECBAgQIAAAQJNKFCwEYFf+9rX4pe//GUWardu3eIzn/lMNipw/9jTQiLp+4Hpu3MPP/xwpEUjLr300oIvRrF/v+/k8aZNm2LUqFGRpkVXlzSyr3fv3tG3b99Iqwmn1ZM7dOiQJf8qKyuzlYQrKipi7dq1sWvXruy2NIJw3LhxWbLVdxarJf0kQIAAAQIECBAgQIAAAQIECBCoj0BBEoFpuusNN9yQ9Xf88cfHggULok+fPgfs/7DDDosLLrggzj///Ljmmmti5syZ8eijj2YLSJx77rkHvKeUT27fvj3GjBmTSwKefPLJceWVV2bTolMCsK6yZ8+e7HuLaTrxnDlzIh1Pnjw5S6amEZgKAQIECBAgQIAAAQIECBAgQIAAgfoIFGRq8JNPPhlbtmzJ+vve975XYxLwzQGlEXFf+tKX4j3veU92Ok0TboklrZCbpgGnct5558XixYuzn/VJAqZ7ysrK4pRTTolbb7015s+fnxtlmb7FWIhVfFMfCgECBAgQIECAAAECBAgQIECAQMsXKEgi8LnnnsukDj300LxWl23Xrl185CMfye595plnWqT2Y489lj1X+r5fGtXXtm3DydMIwOqRl2ma8Zu/t9gi8TwUAQIECBAgQIAAAQIECBAgQIBAwQQanpV6UwhpanAqDUlyde3aNbt369at2c+W9kua9pzKmWeemRvN15hnPPvss3O3r1y5MrdvhwABAgQIECBAgAABAgQIECBAgEBtAgVJBKYFL1JJKwWvWbMm26/vL2lV3FQGDx5c31tKql5aDCWVmr6ZmO/DdO/ePZdQ3LlzZ763q0+AAAECBAgQIECAAAECBAgQINBKBQqSCDzxxBNzowGvu+66elM+/fTT8atf/Sqrn6bOtsQyYMCA7LGqvxPY2GdMU43TgiGpJHeFAAECBAgQIECAAAECBAgQIECAQH0ECpIITKPdRo0alfWXFrX46le/mktW1RRE+iZgmua6Y8eOOOigg7KVdWuqW8rnhw0bloV/5513xiOPPNKoR9m8eXNMmTIla6Nbt27Rv3//RrXnZgIECBAgQIAAAQIECBAgQIAAgdYjUJBEYOL613/91+jQoUMmd/XVV8fAgQPjy1/+cqRVc9NKuWll4QULFkRaVficc86J448/Pp599tms/r/8y7/EoEGDsv2W9sv06dOzqbyVlZVx1llnZav/7t69O+/HXLZsWZx++umRfqYyYcKEvNtwAwECBAgQIECAAAECBAgQIECAQOsVaF+oR3/ve98bs2fPjksuuSRef/317FuBM2bMqLP5tBLu1KlT66xXqhXS1OBZs2Zlz7hly5YsgZeed8SIETF06NBsVF/Pnj2jvLw8OnbsGFVVVZGShmnxlIqKikgrMi9cuDBWrFiRI0gJwWuvvTZ3bIcAAQIECBAgQIAAAQIECBAgQIBAXQIFSwSmji666KJI3/r7x3/8x1iyZEmtfb/rXe+K66+/Ps4999xa67WEi1dddVWkRT4mTpwYaYGPtMry/fffn235Pt/o0aNj3rx5uW8y5nu/+gQIECBAgAABAgQIECBAgAABAq1ToKCJwER40kknxeOPPx6rVq3KEl0rV66M9evXx65du+Loo4+Od7/73dk2cuTIbARca2EfP358jB07Nm666aa4/fbb4+WXX673o6cp1ykBmEZbpjYUAgQIECBAgAABAgQIECBAgAABAvkKFDwRWB3AMcccE5MnT64+9PO/BXr06BEzZ87MtjVr1mTfTkwJ0zQNOE0bTiMFy8rKonPnztGlS5dI04rTtxNPOOGE7BxEAgQIECBAgAABAgQIECBAgAABAg0VaLJEYEMDai339evXL9KmECBAgAABAgQIECBAgAABAgQIEGgOAYnA5lBuQB9pwZX//M//zO5M31Ps379/A1pxCwECBAgQIECAAAECBAgQIECAAIE3BNrmAzFnzpw49NBDs+2DH/xg7tYNGzbkzldfz/fnddddl2vPTsRrr70Wp512WrbddtttSAgQIECAAAECBAgQIECAAAECBAg0SiCvEYG7d+/OvmWXekzftasu+/bty52vPpfvz7SYiEKAAAECBAgQIECAAAECBAgQIECAQNMI5JUIPOSQQ3LftTvyyCNzEbVr1y53Pncyz500grAllueeey6qqqryfrS0cEh1efXVV+PZZ5+tPsz9PPbYY3P7dggQIECAAAECBAgQIECAAAECBAjUJpBXIvD888+PtO1fDjvssFi9evX+px3/t8Cpp54aL7/8cqMsZs+eHWnbv6SRmAoBAgQIECBAgAABAgQIECBAgACB+gjk9Y3AqVOnxuDBg+PSSy+tT9vqECBAgAABAgQIECBAgAABAgQIECBQJAJ5jQh8/vnnY8WKFZFWtH1zSVNXR44cmZ166KGHonv37m++3Kr3r7nmmpgyZUpUVlZmDml69RlnnBFt2rSp1SV9j/Gee+7J6gwaNCiGDBlSa30XCRAgQIAAAQIECBAgQIAAAQIECNQmkFcicP369VlbmzZtijQttTqZlb6B94c//CG7tmfPntr6a3XX/umf/ik+/OEPxwUXXBDLli2L9O2/zZs3x+233x5HHHFEjR5btmzJJQI//vGPx7/+67/WWNcFAgQIECBAgAABAgQIECBAgAABAnUJ5DU1uHpBj/TNu8cff7yutl3/H4E0oi95panVbdu2jQcffDCbYn3XXXcxIkCAAAECBAgQIECAAAECBAgQINAsAnmNCBw6dGg88MADWWCnn356pK1nz56xc+fOXLAzZsyITp065Y7ru5Omy6atpZaDDjoorr/++uwZL7zwwqioqIhPfepT8fOf/zxuueWW6Nq1a0t9dM9FgAABAgQIECBAgAABAgQIECBQBAJ5JQInTJgQP/jBD+Kll17Kprjefffdb3uEOXPmvO1cfU6klYdbciKw2iBNE37yyScjTRn+8Y9/HPPmzYuFCxdmrn/7t39bXc1PAgQIECBAgAABAgQIECBAgAABAgUVyGtq8FFHHRW/+93v4tRTTy1oEK2tsTTF+kc/+lHccccd2UjANDrwIx/5SFx55ZW5RUVam4nnJUCAAAECBAgQIECAAAECBAgQaFqBvEYEplB69eqVjWBL3wlMi4ek1XDTqsFjxozJIk1Th7t165Z31CnJ2NpKWkAkJVU//elPZ6Zf//rX4z/+4z/ihz/8YRx99NGtjcPzEiBAgAABAgQIECBAgAABAgQINKFA3onA6lhSQjBtqVSvJpz2TzrppNz5dKzULtC3b9/49a9/nX0/8Atf+EI89dRTMXz48JgyZUrtN7pKgAABAgQIECBAgAABAgQIECBAIA+BvKYG19Ruhw4dYuzYsdnWsWPHmqo5X4NAWkl42rRpsXjx4jj22GNjz549cd1119VQ22kCBAgQIECAAAECBAgQIECAAAEC+QsUJBGYvnl33333ZVvaVxomkEZT/v73v4+JEyc2rAF3ESBAgAABAgQIECBAgAABAgQIEKhBoMFTg2toz+lGCpSXl8e3vvWt7JuL9957b9ba+973vka26nYCBAgQIECAAAECBAgQIECAAIHWLpBXInDOnDkxefLkzGzQoEHx2GOPZfsbNmyIgQMHNsoyTY1Nm/KGwN/93d9F2hQCBAgQIECAAAECBAgQIECAAAEChRDIKxG4e/fu2LJlS9bv1q1bc/3v27cvdz53Ms+dXbt25XmH6gQIECBAgAABAgQIECBAgAABAgQI1Fcgr0TgIYccEv369cvaPvLII3N9tGvXLnc+dzLPHd8WzBNMdQIECBAgQIAAAQIECBAgQIAAAQJ5COSVCDz//PMjbfuXww47LFavXr3/accECBAgQIAAAQIECBAgQIAAAQIECBSJQEFWDS6SZxEGAQIECBAgQIAAAQIECBAgQIAAAQI1COQ1IrCGNmLPnj3x6KOPZpc/+MEPxkEHHVRT1bed/8lPfhJPP/10nHDCCfGxj33sbdedIECAAAECBAgQIECAAAECBAgQIECg8QIFSQRu3LgxTjvttCyaP//5z9GrV696R3bxxRfHtm3b4h/+4R8kAuutpiIBAgQIECBAgAABAgQIECDwyiuvxOTJk+PNC5oWq0paX+Fzn/tcfPjDHy7WEMXVCgQKkghsqNPOnTsjbam8+uqrDW3GfQQIECBAgAABAgQIECBAgEArFPjVr34Vd9xxR8k8eVlZmURgybytlhlo3onAe+65J9atW/cWjTSir7rMmTMn0urCdZVdu3bFggULoqqqKqt6/PHH13WL6wQIECBAgAABAgQIECBAgACBnMDrr7+e7fcZ+v5476c+mztfbDvrVvwuHp93S+zdu7fYQhNPKxPIOxGY/pBddtllNTLNmDGjxmu1XRg+fHhtl10jQIAAAQIECBAgQIAAAQIECLxFoE2bNtlx58N6xzF/89G3XCumg73/vbaCQqAYBPJeNficc86JUaNGFTT2adOmxZgxYwrapsYIECBAgAABAgQIECBAgAABAgQIEPhfgbxHBKZbv//978fDDz+cayV9lPPyyy/Pjm+66abo2rVr7tqBdlLGvmPHjtG5c+dIU4L79et3oGrOESBAgAABAgQIECBAgAABAgQIECBQIIEGJQL79u0bF110US6E9evX5xKBn/rUp/JaNTjXiB0CBAgQIECAAAECBAgQIECAAAECBJpMoEGJwP2jSYuDpJGAqXTp0mX/y44JECBAgAABAgQIECBAgAABAq1eYOPGjbFkyZKidmjbtm0MGTIkDjrooKKOU3ANEyhIIrBTp065EYHVYWzevDmefvrp+OMf/xjPPvtspFWCe/ToET179owRI0bEMcccU13VTwIECBAgQIAAAQIECBAgQIBAixXY+MJz2bOlz6yVwmKpn/3sZ2P27Nkt9n205gcrSCLwzYAp4Xf99dfHrFmzorKy8s2X3rKfssupjkVC3sLigAABAgQIECBAgAABAgQIEGhhAju3bcmeqKxbpzj43YcV7dPt2bwztq/8S6xbt65oYxRY4wQKmghcvnx5nH322bFq1ao6o3ryySdj7NixkVYM/vKXv1xnfRUIECBAgAABAgQIECBAgAABAqUscNjfvjuG/eSion2E9fc9Ff817raijU9gjRcoWCIwjQT8+7//+1wSsKysLD75yU/GcccdF/37989WCV67dm2k7d57740XXnghi/66667L6lx44YWNfxotECBAgAABAgQIECBAgAABAgQIECBwQIGCJQK/8IUvxFNPPZV1kkb6pcVDBgwYcMBOv/rVr8Z3v/vduPrqq7Ppw5MmTYpPfOIT0blz5wPWd5IAAQIECBAgQIAAAQIECBAgQIAAgcYJtG3c7W/cXVVVFTfffHN28N73vjfuvvvuGpOAqVKHDh3isssuy92zbdu2+PGPf/xGY34lQIAAAQIECBAgQIAAAQIECBAgQKDgAgVJBKaVgdPU4FS++c1v1nuJ6UsvvTSGDRuW3ffggw9mP/1CgAABAgQIECBAgAABAgQIECBAgEDhBQqSCEwLf6SSRvqddNJJeUX5wQ9+MKufvh2oECBAgAABAgQIECBAgAABAgQIECDQNAIFSQRu2fLGMtidOnWq92jA6sfp2rVrtpumFysECBAgQIAAAQIECBAgQIAAAQIECDSNQEESgccee2wW3aZNm+L555/PK9Innngiqz948OC87lOZAAECBAgQIECAAAECBAgQIECAAIH6CxQkETho0KBcj9WLhuRO1LKTphT/+te/zmpIBNYC5RIBAgQIECBAgAABAgQIECBAgACBRgoUJBF4+OGHx+jRo7NQvvGNb8Rtt91WZ1gvvPBCfPKTn4zKyspo37597v46b1SBAAECBAgQIECAAAECBAgQIECAAIG8BQqSCEy93nTTTVFWVpYFcMkll8T73//+uPvuu+Ppp5+OHTt2xN69e6OioiL+8z//My6//PI45phjYtWqVVn96dOnhxGBGYVfCBAgQIAAAQIECBAgQIAAAQIECDSJQPtCtfrXf/3XkUYDXnHFFbFr1654/PHHsxF/1e2nUX8HWhDk5JNPjmuuuaa6mp8ECBAgQIAAAQIECBAgQIAAAQIECDSBQMFGBKbYJkyYEGnxjxNPPPFtoe6fBOzcuXN85Stfid/+9re5kYRvu8kJAgQIECBAgAABAgQIECBAgAABAgQKIlCwEYHV0Rx//PHZaMCf/exn8dRTT8UzzzyTbZs3b44BAwZkU4IHDhwYF1xwQRxxxBHVt/lJgAABAgQIECBAgAABAgQIECBAgEATChQ8EZhiTd8KPPfcc5swbE0TIECAAAECBAgQIECAAAECBAgQIJCPQEGnBufTsboECBAgQIAAAQIECBAgQIAAAQIECDSfgERg81nriQABAgQIECBAgAABAgQIECBAgMA7JlDwqcGvvfZa/OQnP4k//vGPsX379myl4H379tX5gGPHjo20KQQIECBAgAABAgQIECBAgAABAgQIFF6goInA66+/PmbOnBlbt27NO9JevXpJBOat5gYCBAgQIECAAAECBAgQIECAAAEC9RMoWCJw/vz58fnPf75+vapFgAABAgQIECBAgAABAgQIECBAgECzChQkEbh37964+OKLc4GfdNJJMWHChOjfv38cfPDB0aZNm9y1mnaOOuqomi45T4AAAQIECBAgQIAAAQIECBAgQIBAIwUKkghM3wPcuHFjFspHP/rRuOeee6JTp06NDM3tBAgQIECAAAECBAgQIECAAAECBAgUSqAgqwb//ve/z8UzZcoUScCchh0CBAgQIECAAAECBAgQIECAAAECxSFQkERgVVVV9jRpCvApp5xSHE8mCgIECBAgQIAAAQIECBAgQIAAAQIEcgIFSQR+4AMfyBrct29fbopwrgc7BAgQIECAAAECBAgQIECAAAECBAi84wIFSQQOHDgwunfvnj3MQw899I4/lAAIECBAgAABAgQIECBAgAABAgQIEHirQEESganJa665Jmt5xowZ8corr7y1F0cECBAgQIAAAQIECBAgQIAAAQIECLyjAgVLBF5++eWRkoDr1q2LY489Nr797W/Hs88+Gzt37nxHH1DnBAgQIECAAAECBAgQIECAAAECBAhEtC8EwpYtW2LixIlZU+Xl5fHqq6/mjtPJQw89NNq1a1drV1dffXWkTSFAgAABAgQIECBAgAABAgQIECBAoPACBUkEVlZWxrx582qMbvPmzTVeq75g5GC1hJ8ECBAgQIAAAQIECBAgQIAAAQIECi9QkERg27Zt46ijjmpUdF26dGnU/W4mQIAAAQIECBAgQIAAAQIECBAgQKBmgYIkAnv06BEVFRU19+IKAQIECBAgQIAAAQIECBAgQIAAAQLvqEDBFgt5R59C5wQIECBAgAABAgQIECBAgAABAgQI1CogEVgrj4sECBAgQIAAAQIECBAgQIAAAQIEWoaARGDLeI+eggABAgQIECBAgAABAgQIECBAgECtAhKBtfK4SIAAAQIECBAgQIAAAQIECBAgQKBlCEgEtoz36CkIECBAgAABAgQIECBAgAABAgQI1CogEVgrj4sECBAgQIAAAQIECBAgQIAAAQIEWoaARGDLeI+eggABAgQIECBAgAABAgQIECBAgECtAhKBtfK4SIAAAQIECBAgQIAAAQIECBAgQKBlCEgEtoz36CkIECBAgAABAgQIECBAgAABAgQI1CogEVgrj4sECBAgQIAAAQIECBAgQIAAAQIEWoaARGDLeI+eggABAgQIECBAgAABAgQIECBAgECtAu1rvepikwusW7cuNm7cGDt27Mi2jh07RteuXaNLly7RvXv3SMcKAQIECBAgQIAAAQIECBAgQIAAgcYKSAQ2VjDP+7dt2xZz586NefPmxYoVKyId11Tat28fgwcPjuHDh8fYsWPjjDPOiDZt2tRU3XkCBAgQIECAAAECBAgQIECAAAECNQqYGlwjTWEvrF+/PiZOnBhHHnlkTJo0KRYtWlRrEjD1XlVVFUuXLo3Zs2dnicAhQ4bEAw88UNjAtEaAAAECBAgQIECAAAECBAgQINAqBIwIbIbXvGnTphg1alQsX74811sa2de7d+/o27dv9OjRI8rLy6NDhw5Z8q+ysjK2bt0aFRUVsXbt2ti1a1d2XxpBOG7cuLjxxhvjiiuuyLVlhwABAgQIECBAgAABAgQIECBAgEBdAhKBdQk18vr27dtjzJgxuSTgySefHFdeeWWMHDkySwDW1fyePXtiyZIl2XTiOXPmRDqePHlyDBw4MJsqXNf9rhMgQIAAAQIECBAgQIAAAQIECBBIAqYGN/Hvg7vuuiubBpy6Oe+882Lx4sXZzzQKsD6lrKwsTjnllLj11ltj/vz5kY5TmTZtWuzdu7c+TahDgAABAgQIECBAgAABAgQIECBAQCKwqX8PPPbYY1kX6ft+aZGQtm0bnntNi4XccMMNWXtpmvHq1aubOnztEyBAgAABAgQIECBAgAABAgQItBCBhmelWghAUz/Go48+mnVx5pln5kbzNabPs88+O3f7ypUrc/t2CBAgQIAAAQIECBAgQIAAAQIECNQmIBFYm04Brr344otZK3369ClAaxHdu3fPJRR37txZkDY1QoAAAQIECBAgQIAAAQIECBAg0PIFJAKb+B0PGDAg62HRokUF6SlNNU4LhqRy4oknFqRNjRAgQIAAAQIECBAgQIAAAQIECLR8AYnAJn7Hw4YNy3q4884745FHHmlUb5s3b44pU6ZkbXTr1i369+/fqPbcTIAAAQIECBAgQIAAAQIECBAg0HoEJAKb+F1Pnz49m8pbWVkZZ511Vrb67+7du/PuddmyZXH66adH+pnKhAkT8m7DDQQIECBAgAABAgQIECBAgAABAq1XoH3rffTmefI0NXjWrFkxderU2LJlS5bAS/sjRoyIoUOHZqP6evbsGeXl5dGxY8eoqqqKlDTcunVrVFRUxHPPPRcLFy6MFStW5AJOCcFrr702d2yHAAECBAgQIECAAAECBAgQIECAQF0CEoF1CRXg+lVXXZUt8jFx4sRIC3xs27Yt7r///mzLt/nRo0fHvHnzom1bgznztVOfAAECBAgQIECAAAECBAgQINCaBWSTmuntjx8/PtauXRszZsyIXr165dVrhw4dsmnF9913XyxYsCDS9wEVAgQIECBAgAABAgQIECBAgAABAvkIGBGYj1Yj6/bo0SNmzpyZbWvWrInFixfHqlWrsmnAadpwGilYVlYWnTt3ji5dukSaVjxo0KA44YQTsnON7N7tBAgQIECAAAECBAgQIECAAAECrVhAIvAdevn9+vWLtCkECBAgQIAAAQIECBAgQIAAAQIEmkPA1ODmUNYHAQIECBAgQIAAAQIECBAgQIAAgXdYwIjAd/gFrFu3LjZu3Bg7duzItrRycNeuXbOpwd27d89WEn6HQ9Q9AQIECBAgQIAAAQIECBAgQIBACxCQCGzml5i+Azh37txs5d8VK1Zk3wWsKYT27dvH4MGDY/jw4TF27Ng444wzok2bNjVVd54AAQIECBAgQIAAAQIECBAgQIBAjQKmBtdIU9gL69evj4kTJ8aRRx4ZkyZNikWLFtWaBEy9V1VVxdKlS2P27NlZInDIkCHxwAMPFDYwrREgQIAAAQIECBAgQIAAAQIECLQKASMCm+E1b9q0KUaNGhXLly/P9ZZG9vXu3Tv69u0baTXh8vLy6NChQ5b8q6yszFYSrqioiLVr18auXbuy+9IIwnHjxsWNN94YV1xxRa4tOwQIECBAgAABAgQIECBAgAABAgTqEpAIrEuokde3b98eY8aMySUBTz755Ljyyitj5MiRWQKwrub37NkTS5YsyaYTz5kzJ9Lx5MmTY+DAgdlU4brud50AAQIECBAgQIAAAQIECBAgQIBAEjA1uIl/H9x1113ZNODUzXnnnReLFy/OfqZRgPUpZWVlccopp8Stt94a8+fPj3ScyrRp02Lv3r31aUIdAgQIECBAgAABAgQIECBAgAABAhKBTf174LHHHsu6SN/3S4uEtG3b8NxrWizkhhtuyNpL04xXr17d1OFrnwABAgQIECBAgAABAgQIECBAoIUINDwr1UIAmvoxHn300ayLM888MzearzF9nn322bnbV65cmdu3Q4AAAQIECBAgQIAAAQIECBAgQKA2AYnA2nQKcO3FF1/MWunTp08BWovo3r17LqG4c+fOgrSpEQIECBAgQIAAAQIECBAgQIAAgZYvIBHYxO94wIABWQ+LFi0qSE9pqnFaMCSVE088sSBtaoQAAQIECBAgQIAAAQIECBAgQKDlC0gENvE7HjZsWNbDnXfeGY888kijetu8eXNMmTIla6Nbt27Rv3//RrXnZgIECBAgQIAAAQIECBAgQIAAgdYjIBHYxO96+vTp2VTeysrKOOuss7LVf3fv3p13r8uWLYvTTz890s9UJkyYkHcbbiBAgAABAgQIECBAgAABAgQIEGi9Au1b76M3z5OnqcGzZs2KqVOnxpYtW7IEXtofMWJEDB06NBvV17NnzygvL4+OHTtGVVVVpKTh1q1bo6KiIp577rlYuHBhrFixIhdwSghee+21uWM7BAgQIECAAAECBAgQIECAAAECBOoSkAisS6gA16+66qpskY+JEydGWuBj27Ztcf/992dbvs2PHj065s2bF23bGsyZr536BAgQIECAAAECBAgQIECAAIHWLCCb1Exvf/z48bF27dqYMWNG9OrVK69eO3TokE0rvu+++2LBggWRvg+oECBAgAABAgQIECBAgAABAgQIEMhHwIjAfLQaWbdHjx4xc+bMbFuzZk0sXrw4Vq1alU0DTtOG00jBsrKy6Ny5c3Tp0iXStOJBgwbFCSeckJ1rZPcNvv22226L73znO7Fv374Gt1F9Y5runMr69eurT/lJgAABAgQIECBAgAABAgQIECDQDAISgc2AfKAu+vXrF2krhfLwww/HE088UdBQX3755YK2pzECBAgQIECAAAECBAgQIECAAIHaBSQCa/dp8qvr1q2LjRs3xo4dO7ItLRjStWvXbERg9+7dswVEmjyIOjq4/fbbs8VO9u7dW0fNui/feOON8aMf/Sgb5Vh3bTUIECBAgAABAgQIECBAgAABAgQKJSARWCjJeraTpv/OnTs3W/AjrQScjmsq7du3j8GDB8fw4cNj7NixccYZZ0SbNm1qqt5k59M3CtMKx4Uohx9+eCGa0QYBAgQIECBAgAABAgQIECBAgECeAhYLyROsodXTN/HSqsFHHnlkTJo0KRYtWlRrEjD1U1VVFUuXLo3Zs2dnicAhQ4bEAw880NAQ3EeAAAECBAgQIECAAAECBAgQINCKBYwIbIaXv2nTphg1alQsX74811sa2de7d+/o27dvpEVEysvLI428S8m/ysrKbAGRtLBGWml4165d2X1pBOG4ceMiTa+94oorcm3ZIUCAAAECBAgQIECAAAECBAgQIFCXgERgXUKNvL59+/YYM2ZMLgl48sknx5VXXhkjR47MEoB1Nb9nz55YsmRJNp14zpw5kY4nT54cAwcOzKYK13W/6wQIECBAgAABAgQIECBAgAABAgSSgKnBTfz74K677sqmAaduzjvvvFi8eHH2M40CrE8pKyuLU045JW699daYP39+pONUpk2bFoVYvKM+MahDgAABAgQIECBAgAABAgQIECBQ+gISgU38Dh977LGsh/R9v7RISNu2DSdPi4XccMMNWXtpmvHq1aubOHrNEyBAgAABAgQIECBAgAABAgQItBSBhmelWopAEz/Ho48+mvVw5pln5kbzNabLs88+O3f7ypUrc/t2CBAgQIAAAQIECBAgQIAAAQIECNQmIBFYm04Brr344otZK3369ClAaxHdu3fPJRR37txZkDY1QoAAAQIECBAgQIAAAQIECBAg0PIFJAKb+B0PGDAg62HRokUF6SlNNU4LhqRy4oknFqRNjRAgQIAAAQIECBAgQIAAAQIECLR8AYnAJn7Hw4YNy3q4884745FHHmlUb5s3b44pU6ZkbXTr1i369+/fqPbcTIAAAQIECBAgQIAAAQIECBAg0HoEJAKb+F1Pnz49m8pbWVkZZ511Vrb67+7du/PuddmyZXH66adH+pnKhAkT8m7DDQQIECBAgAABAgQIECBAgAABAq1XoH3rffTmefI0NXjWrFkxderU2LJlS5bAS/sjRoyIoUOHZqP6evbsGeXl5dGxY8eoqqqKlDTcunVrVFRUxHPPPRcLFy6MFStW5AJOCcFrr702d2yHAAECBAgQIECAAAECBAgQIECAQF0CEoF1CRXg+lVXXZUt8jFx4sRIC3xs27Yt7r///mzLt/nRo0fHvHnzom1bgznztVOfAAECBAgQIECAAAECBAgQINCaBWSTmuntjx8/PtauXRszZsyIXr165dVrhw4dsmnF9913XyxYsCDS9wEVAgQIECBAgAABAgQIECBAgAABAvkIGBGYj1Yj6/bo0SNmzpyZbWvWrInFixfHqlWrsmnAadpwGilYVlYWnTt3ji5dukSaVjxo0KA44YQTsnON7N7tBAgQIECAAAECBAgQIECAAAECrVhAIvAdevn9+vWLtCkECBAgQIAAAQIECBAgQIAAAQIEmkPA1ODmUNYHAQIECBAgQIAAAQIECBAgQIAAgXdYwIjAd+gF7Nq1K9K3//Itr776arbgSLrvqKOOyvd29QkQIECAAAECBAgQIECAAAECBFqpgBGBzfjif/rTn8bf/d3fRc+ePaO8vDyOO+64uPDCC+PRRx+tdxQXXXRR9OnTJ9vqfZOKBAgQIECAAAECBAgQIECAAAECrV5AIrAZfgts3749PvOZz8Q555wT//7v/x4bNmyIffv2xbPPPhs//OEP40Mf+lBceeWVuZF+zRCSLggQIECAAAECBAgQIECAAAECBFqZgERgM7zwGTNmxNy5c3M9HXzwwdG/f/9o06ZNdm7v3r3x9a9/PYYOHRqrV6/O1bNDgAABAgQIECBAgAABAgQIECBAoFACEoGFkqyhnWXLlsUtt9ySXU1Tgu+9997YunVrPP/887Fp06a4/vrro2vXrtn1lStXxoc//GHJwBosnSZAgAABAgQIECBAgAABAgQIEGi4gERgw+3qded3vvOdeP3116N9+/bx4IMPxrhx46Jt2zfYUwJw6tSp8cwzz8QJJ5yQtffCCy/EyJEjY/369fVqXyUCBAgQIECAAAECBAgQIECAAAEC9RGQCKyPUiPqpCRfKueff34u2bd/c717946FCxfGiBEjsktpevCYMWMifVtQIUCAAAECBAgQIECAAAECBAgQIFAIAYnAQijW0sYf//jH7OqwYcNqqRXRpUuXWLBgQXzgAx/I6j3xxBNx7rnnZqMJa73RRQIECBAgQIAAAQIECBAgQIAAAQL1EJAIrAdSY6rs3r07u71Tp051NlNeXh4///nP493vfndW9xe/+EVcdtlldd6nAgECBAgQIECAAAECBAgQIECAAIG6BCQC6xJq5PVjjjkma+Hpp5+uV0uHHXZY/Pu//3v06NEjq5++Mfi1r32tXveqRIAAAQIECBAgQIAAAQIECBAgQKAmAYnAmmQKdL46EThv3rzYuHFjvVodMGBANjIwjRBM5aqrroq5c+fW616VCBAgQIAAAQIECBAgQIAAAQIECBxIQCLwQCoFPJcWCUllw4YN2YIh9V0N+P3vf3+k5GFaYXjfvn0xfvz4+NKXvhR79+4tYHSaIkCAAAECBAgQIECAAAECBAgQaC0CEoFN/KbT6r8f+chHsl4efPDBOO6447Kk3re+9a06e/74xz8e3/72t6NNmzZZAvCLX/xiNm24zhtVIECAAAECBAgQIECAAAECBAgQILCfgETgfiBNcXjrrbfG4MGDs6Y3bdoUP/jBD2L27Nn16uqzn/1s3H777dG+ffusvhGB9WJTiQABAgQIECBAgAABAgQIECBAYD8BicD9QJri8Oijj44lS5bExIkT4+CDD866OOKII+rd1UUXXRRLly6NU089td73qEiAAAECBAgQIECAAAECBAgQIEDgzQISgW/WaML9jh07RpoOvHnz5li0aFG2AEg+3b3nPe+JhQsXxr/9279F+n5gly5d8rldXQIECBAgQIAAAQIECBAgQIAAgVYu8MZ801aO0JyPn6b4pkReQ8uFF14YaVMIECBAgAABAgQIECBAgAABAgQI5CNgRGA+WuoSIECAAAECBAgQIECAAAECBAgQKFEBicASfXHCJkCAAAECBAgQIECAAAECBAgQIJCPgERgPlrqEiBAgAABAgQIECBAgAABAgQIEChRAYnAEn1xwiZAgAABAgQIECBAgAABAgQIECCQj4BEYD5a6hIgQIAAAQIECBAgQIAAAQIECBAoUQGJwBJ9ccImQIAAAQIECBAgQIAAAQIECBAgkI+ARGA+WuoSIECAAAECBAgQIECAAAECBAgQKFEBicASfXHCJkCAAAECBAgQIECAAAECBAgQIJCPgERgPlrqEiBAgAABAgQIECBAgAABAgQIEChRAYnAEn1xwiZAgAABAgQIECBAgAABAgQIECCQj4BEYD5a6hIgQIAAAQIECBAgQIAAAQIECBAoUQGJwBJ9ccImQIAAAQIECBAgQIAAAQIECBAgkI+ARGA+WuoSIECAAAECBAgQIECAAAECBAgQKFEBicASfXHCJkCAAAECBAgQIECAAAECBAgQIJCPgERgPlrqEiBAgAABAgQIECBAgAABAgQIEChRAYnAEn1xwiZAgAABAgQIECBAgAABAgQIECCQj4BEYD5a6hIgQIAAAQIECBAgQIAAAQIECBAoUQGJwBJ9ccImQIAAAQIECBAgQIAAAQIECBAgkI+ARGA+WuoSIECAAAECBAgQIECAAAECBAgQKFEBicASfXHCJkCAAAECBAgQIECAAAECBAgQIJCPgERgPlrqEiBAgAABAgQIECBAgAABAgQIEChRAYnAEn1xwiZAgAABAgQIECBAgAABAgQIECCQj4BEYD5a6hIgQIAAAQIECBAgQIAAAQIECBAoUQGJwBJ9ccImQIAAAQIECBAgQIAAAQIECBAgkI+ARGA+WuoSIECAAAECBAgQIECAAAECBAgQKFEBicASfXHCJkCAAAECBAgQIECAAAECBAgQIJCPgERgPlrqEiBAgAABAgQIECBAgAABAgQIEChRAYnAEn1xwiZAgAABAgQIECBAgAABAgQIECCQj4BEYD5a6hIgQIAAAQIECBAgQIAAAQIECBAoUQGJwBJ9ccImQIAAAQIECBAgQIAAAQIECBAgkI+ARGA+WuoSIECAAAECBAgQIECAAAECBAgQKFEBicASfXHCJkCAAAECBAgQIECAAAECBAgQIJCPgERgPlrqEiBAgAABAgQIECBAgAABAgQIEChRAYnAEn1xwiZAgAABAgQIECBAgAABAgQIECCQj4BEYD5a6hIgQIAAAQIECBAgQIAAAQIECBAoUQGJwBJ9ccImQIAAAQIECBAgQIAAAQIECBAgkI+ARGA+WuoSIECAAAECBAgQIECAAAECBAgQKFEBicASfXHCJkCAAAECBAgQIECAAAECBAgQIJCPgERgPlrqEiBAgAABAgQIECBAgAABAgQIEChRAYnAEn1xwiZAgAABAgQIECBAgAABAgQIECCQj4BEYD5a6hIgQIAAAQIECBAgQIAAAQIECBAoUQGJwBJ9ccImQIAAAQIECBAgQIAAAQIECBAgkI+ARGA+WuoSIECAAAECBAgQIECAAAECBAgQKFEBicASfXHCJkCAAAECBAgQIECAAAECBAgQIJCPgERgPlrqEiBAgAABAgQIECBAgAABAgQIEChRAYnAEn1xwiZAgAABAgQIECBAgAABAgQIECCQj4BEYD5a6hIgQIAAAQIECBAgQIAAAQIECBAoUQGJwBJ9ccImQIAAAQIECBAgQIAAAQIECBAgkI+ARGA+WuoSIECAAAECBAgQIECAAAECBAgQKFEBicASfXHCJkCAAAECBAgQIECAAAECBAgQIJCPgERgPlrqEiBAgAABAgQIECBAgAABAgQIEChRAYnAEn1xwiZAgAABAgQIECBAgAABAgQIECCQj4BEYD5a6hIgQIAAAQIECBAgQIAAAQIECBAoUQGJwBJ9ccImQIAAAQIECBAgQIAAAQIECBAgkI+ARGA+WuoSIECAAAECBAgQIECAAAECBAgQKFEBicASfXHCJkCAAAECBAgQIECAAAECBAgQIJCPgERgPlrqEiBAgAABAgQIECBAgAABAgQIEChRAYnAEn1xwiZAgAABAgQIECBAgAABAgQIECCQj4BEYD5a6hIgQIAAAQIECBAgQIAAAQIECBAoUQGJwBJ9ccImQIAAAQIECBAgQIAAAQIECBAgkI+ARGA+WuoSIECAAAECBAgQIECAAAECBAgQKFEBicASfXHCJkCAAAECBAgQIECAAAECBAgQIJCPgERgPlrqEiBAgAABAgQIECBAgAABAgQIEChRAYnAEn1xwiZAgAABAgQIECBAgAABAgQIECCQj4BEYD5a6hIgQIAAAQIECBAgQIAAAQIECBAoUQGJwBJ9ccImQIAAAQIECBAgQIAAAQIECBAgkI+ARGA+WuoSIECAAAECBAgQIECAAAECBAgQKFEBicASfXHCJkCAAAECBAgQIECAAAECBAgQIJCPgERgPlrqEiBAgAABAgQIECBAgAABAgQIEChRAYnAEn1xwiZAgAABAgQIECBAgAABAgQIECCQj4BEYD5a6hIgQIAAAQIECBAgQIAAAQIECBAoUQGJwBJ9ccImQIAAAQIECBAgQIAAAQIECBAgkI+ARGA+WuoSIECAAAECBAgQIECAAAECBAgQKFEBicASfXHCJkCAAAECBAgQIECAAAECBAgQIJCPgERgPlrqEiBAgAABAgQIECBAgAABAgQIEChRAYnAEn1xwiZAgAABAgQIECBAgAABAgQIECCQj4BEYD5a6hIgQIAAAQIECBAgQIAAAQIECBAoUQGJwBJ9ccImQIAAAQIECBAgQIAAAQIECBAgkI+ARGA+WuoSIECAAAECBAgQIECAAAECBAgQKFEBicASfXHCJkCAAAECBAgQIECAAAECBAgQIJCPgERgPlrqEiBAgAABAgQIECBAgAABAgQIEChRAYnAEn1xwiZAgAABAgQIECBAgAABAgQIECCQj4BEYD5a6hIgQIAAAQIECBAgQIAAAQIECBAoUQGJwBJ9ccImQIAAAQIECBAgQIAAAQIECBAgkI+ARGA+WuoSIECAAAECBAgQIECAAAECBAgQKFEBicASfXHCJkCAAAECBAgQIECAAAECBAgQIJCPQPt8KqtbeIF169bFxo0bY8eOHdnWsWPH6Nq1a3Tp0iW6d+8e6VghQIAAAQIECBAgQIAAAQIECBAg0FgBicDGCuZ5/7Zt22Lu3Lkxb968WLFiRaTjmkr79u1j8ODBMXz48Bg7dmycccYZ0aZNm5qqO0+AAAECBAgQIECAAAECBAgQIECgRgFTg2ukKeyF9evXx8SJE+PII4+MSZMmxaJFi2pNAqbeq6qqYunSpTF79uwsEThkyJB44IEHChuY1ggQIECAAAECBAgQIECAAAECBFqFgBGBzfCaN23aFKNGjYrly5fneksj+3r37h19+/aNHj16RHl5eXTo0CFL/lVWVsbWrVujoqIi1q5dG7t27cruSyMIx40bFzfeeGNcccUVubbsECBAgAABAgQIECBAgAABAgQIEKhLQCKwLqFGXt++fXuMGTMmlwQ8+eST48orr4yRI0dmCcC6mt+zZ08sWbIkm048Z86cSMeTJ0+OgQMHZlOF67rfdQIECBAgQIAAAQIECBAgQIAAAQJJwNTgJv59cNddd2XTgFM35513XixevDj7mUYB1qeUlZXFKaecErfeemvMnz8/0nEq06ZNi71799anCXUIECBAgAABAgQIECBAgAABAgQIhBGBTfyb4LHHHst6SN/3S4uEtG3b8NxrWizkhhtuiMsvvzwbYbh69eoYMGBAEz9BxHe+85341re+VZDEY/pWYippteRiLT+e9Ilo2654/2jse/31jG7xqNnRpqxdsTLGjtUbiza2AwX2sxnjo11ZhwNdKopzVbsrszieOOcH0bbjG/9BoCgC2y+InWs37XemuA9/MWtylHUsL9ogK7dtzmJ78tKfRLuDDyreOF98I86iDXC/wB7+xv+N337/q/udLZ7DbRv+nAXzzOfvj1Uzf1U8ge0Xya4/b93vTHEfPvaDr8UTP72taIPc8ucXstie+/JDsfbWRUUb5+4NrxVtbAcKLL3zp3/5swNdKopzWze8lMWxdvZj8ee7nyyKmA4UxJ6NOw50umjPPfXgT2PNkkeKNr7XXnk5i23d/7c0Xv3Nn4o2zqotO4s2tjcHtm/fvuzwT4/9Mr5/wYfefKmo9ndseiWLZ8ODf4xfH3ddUcX25mBef+2NT5O9+Zz9liVQvNmOFuL86KOPZk9y5pln5kbzNebRzj777CwRmNpYuXJlsyQC0yjGp59+ujFhv+3edu2KL4HVr1+/LEG5+aU1b4u3GE/s+NOrxRjWW2LqdHCnOOyww95yrtgO0nt/8sknY8ufK4ottAPGs3NN8Sfayg4qiyOOOOKA8RfLyfTeU9n2P/8Ayw6K+JfKiuJPtLVp2yb77m0RM0b//v2z8Kr/AVbMsabYdq3bmm3FHmf1n6dijbM6vu0b/xJpK/aSEm2lkGyrdi1Wz+r4dm7ZGGkr9rLn1R2RtmIv1a7FGmd1fLu2bYm0FXup2lwZaSv2Uu1arHG+613vykLbveO12PjCc8UaZi6u17ftiu3PbsgdF+tO9d+bijU+cTVcoM1/Z8/fSJ83vA131iLQpUuXbHXgtPLvZz/72Vpq1u9SWkgktZm+FXj33XfHJz7xifrd2Ihaqa/nn38+CvVbJS2E8r73va8RETXNrTt37swWZ2ma1gvXalo8JsV66KGHFq7RJmopTYHv3r17E7VemGZ3796d/f4uTGtN10r6c/jaa6/FX/3VXzVdJwVquVu3bnH44YcXqLWmaSatyv6nP/2pYP+71jRRRrz+3yOAN2/eXPR/jtLzd+3aNVsEq6ksCtFu+qTGc889V5AR7oWIp6Y20v/fvvLKK/X6lnBNbTTX+YMPPjj69OnTXN01uJ/03tOf+2IvGzZsKPr//UyGHTt2jGJPDKQ4099f0//PF3tJM2Z69uxZ7GFmgxqaYzZSYyHSYovp78rFXkrlz3sawPHud7870mKXxVxeeuml7N/dxRxjiu0vf/lL9ve6xswUbI5nTPGlP+/FOICnOZ6/pfchEdjEb/jEE0+MZcuWxWc+85n4wQ9+0OjeHn744WyhkdRQ+suNLH2jSTVAgAABAgQIECBAgAABAgQIEGgVAg3/YF2r4Gn8Qw4bNixr5M4774xHHnmkUQ2mUSFTpkzJ2kgjbiQBG8XpZgIECBAgQIAAAQIECBAgQIBAqxKQCGzi1z19+vRsGH2a0nvWWWdlq/82ZIpCGlV4+umnZ6MLU8gTJkxo4sg1T4AAAQIECBAgQIAAAQIECBAg0JIETA1uhreZVvqdOnVqrqdDDjkkRowYEUOHDs1G9aVvgpSXl2ffW0nfr0lJw/QdvYqKiuxbRgsXLowVK1bk7k8JwQULFjRqBeJcY3YIECBAgAABAgQIECBAgAABAgRahYBEYDO95jlz5sTEiRMb/eHa0aNHx7x58yJNDVYIECBAgAABAgQIECBAgAABAgQI1FdAIrC+UgWol1YIuummm+L222+Pl19+ud4tdujQIVIC8JJLLomxY8fW+z4VCRAgQIAAAQIECBAgQIAAAQIECFQLSARWSzTzzzVr1sTixYtj1apV2TTgLVu2ZMudl5WVRefOnaNLly7Zct2DBg2KE044ITvXzCHqjgABAgQIECBAgAABAgQIECBAoAUJSAS2oJfpUQgQIECAAAECBAgQIECAAAECBAjUJGDV4JpknCdAgAABAgQIECBAgAABAgQIECDQggQkAlvQy/QoBAgQIECAAAECBAgQIECAAAECBGoSkAisScZ5AgQIECBAgAABAgQIECBAgAABAi1IQCKwBb1Mj0KAAAECBAgQIECAAAECBAgQIECgJgGJwJpknCdAgAABAgQIECBAgAABAgQIECDQggQkAlvQy/QoBAgQIECAAAECBAgQIECAAAECBGoSkAisScZ5AgQIECBAgAABAgQIECBAgAABAi1IQCKwBb1Mj0KAAAECBAgQIECAAAECBAgQIECgJgGJwJpknCdAgAABAgQIECBAgAABAgQIECDQggQkAlvQy/QoBAgQIECAAAECBAgQIECAAAECBGoSkAisScZ5AgQIECBAgAABAgQIECBAgAABAi1IQCKwBb1Mj0KAAAECBAgQIECAAAECBAgQIECgJgGJwJpknCdAgAABAgQIECBAgAABAgQIECDQggQkAlvQy/QoBAgQIECAAAECBAgQIECAAAECBGoSkAisScZ5AgQIECBAgAABAgQIECBAgAABAi1IQCKwBb1Mj0KAAAECBAgQIECAAAECBAgQIECgJgGJwJpknCdAgAABAgQIECBAgAABAgQIECDQggQkAlvQy/QoBAgQIECAAAECBAgQIECAAAECBGoSkAisScZ5AgQIECBAgAABAgQIECBAgAABAi1IQCKwBb1Mj0KAAAECBAgQIECAAJCYQXkAADcBSURBVAECBAgQIECgJgGJwJpknCdAgAABAgQIECBAgAABAgQIECDQggQkAlvQy/QoBAgQIECAAAECBAgQIECAAAECBGoSkAisScZ5AgQIECBAgAABAgQIECBAgAABAi1IQCKwBb1Mj0KAAAECBAgQIECAAAECBAgQIECgJgGJwJpknCdAgAABAgQIECBAgAABAgQIECDQggQkAlvQy/QoBAgQIECAAAECBAgQIECAAAECBGoSkAisScZ5AgQIECBAgAABAgQIECBAgAABAi1IQCKwBb1Mj0KAAAECBAgQIECAAAECBAgQIECgJgGJwJpknCdAgAABAgQIECBAgAABAgQIECDQggQkAlvQy/QoBAgQIECAAAECBAgQIECAAAECBGoSkAisScZ5AgQIECBAgAABAgQIECBAgAABAi1IQCKwBb1Mj0KAAAECBAgQIECAAAECBAgQIECgJgGJwJpknCdAgAABAgQIECBAgAABAgQIECDQggQkAlvQy/QoBAgQIECAAAECBAj8/+3dCfxVc/7H8S9lqTSFLJN17CTL2BpkiZBlirGFESoxdpkYyx+N3ZRhFBPGvgwhS/bd2CKJSJRIZDe2inD+3/fXfI9z9/v7dU7OPef1fTzqnnvuueee8/zc37nnfM53QQABBBBAAAEEKgmQCKwkw3wEEEAAAQQQQAABBBBAAAEEEEAAAQQyJEAiMEPBZFcQQAABBBBAAAEEEEAAAQQQQAABBBCoJEAisJIM8xFAAAEEEEAAAQQQQAABBBBAAAEEEMiQQMsM7Qu7gsA8Ffj000/N0KFDzdixY82bb75pZsyYYX7zm9+Y1Vdf3XTt2tUcdthhZsEFF6y5TVOmTDFnn322W4+ml112WbPpppuazTbbzOy5556mTZs2NddRboGnn37abceiiy5qPvnkk3KLlMz76KOPzLnnnmvGjBljXn/9dfPtt9+a5Zdf3uywww6mf//+bt9K3pSzGVmMe6UQDh8+3AwePNi9/J///MesssoqlRbN/Pysxf3HH380hx9+uNFjPUXHom7dutWzaKaWiSvuP/zwg7nqqqvMjTfe6H4vvvzyS7Pxxhu74/yOO+5oNtxww2a5Nec4rw/SsV3b8/zzz5sXXnjBTJ482aywwgpmjTXWMH/605/MNtts06ztycqbshL30aNHm9tvv71ZYRkwYIDZaKONmvXeRn1T2uMu1x49epj77rvP/OMf/3DH8HqsdU538cUXm4kTJ5pJkyaZdu3ambXWWstssskm5s9//rNp27ZtPavJ9DL6W7nhhhvc8Vnn9Dp/1/n8mmuuaQ499FCz3nrr1dz/pI7z+uDmxP2OO+4w+vfSSy+5uC+99NJuf/r162d69uxp5ptvvpr7lPUF0hT3uI4/SV1XZv27kMv9CygIINBkgQsvvDBo3759YA8aFf/ZE4jg0Ucfrbru888/P1hggQUqruN3v/td8Nlnn1VdR7kX9Z7VVlvNrXfxxRcvt0jJvBEjRgS/+tWvKm6LTUgG9uSz5H15mpHFuFeK36uvvhosvPDC4ffBXkBUWjTz87MYd8Wz2vGr+LULLrgg83Eu3sG44j59+vRg7bXXrujdsmXL4Nprry3++JrPm3Oc10qnTZsW2ARPxe1R7Hfbbbdg1qxZNbchiwtkKe5nnHFG1TgX/51Hn990001ZDG/FfUp73LXhw4YNC+NpE4EV98W/YBP+wSGHHBLMP//84fuiMdZ0x44dA5ss8m/J3aNNmgTbbrttRR8ZtWjRIjjyyCODr7/+uqJPUsd5fWBT4/75558H++67b9V9solN91tQcYcy/kLa4h7X8Sep68qMfx1yu3smt3vOjiPQTIHrrruu4MfV3qULzjrrrMDWrghOPvnkwN5lDV9v1apVoIRKufKvf/0rXE4Jlz59+gSXXnppcNJJJwXrrrtu+Frnzp2DDz74oNwqys774osvCi7y6kkE3n333YG9Mxh+5l577RXY2mDBFVdc4U4ifbJSj9dff33Zz836zCzGvVLMdPGgk8ToBUNeE4FZjbut+VAQ32isy03nLREYV9x1PF5nnXVCa/1dnXLKKe5Yv88++wT6jZC3jr+2xk6lP8mS+c05zmslzz77bNChQ4dwe2wt9uDYY48NrrzySneh27p16/A1Wwu85HOzPiNrcZ+bROADDzyQ9XCH+5f2uGtDde4VTejVkwgcOHBg+Pe80EILBbYWuPtbV9Ihmvyytd+CcePGhR55mdDNDp1j+9+8JZdcMjjmmGOCyy67LJDR/vvvH+hGjX9dz8uVpI7z+qymxv27774r2CfFVtutc/rzzjsv2G677cL9WWaZZYI33nij3C5lel7a4h7X8Sep68pMfxlyvnMkAnP+BWD3mybw1ltvBbYJhfsRVVLMNrkpWYF+hI844ojwh1YXfpoXLbYJbngBaJtoBI899lj0Zbf83nvvHa7DNtUqeL3SE9tMLLDNGML36eSlViLQVkUPtA1aVieZd911V8nqX3zxxcBfIOqk6J133ilZJsszshj3avGyTYUKvkP6buQxEZjluA8aNCiMsWr62q4Nqv775ptvqn1lMvVaXHEXSvRC3DavDpRkj5Ynn3wyPP7q2Pree+9FXy473ZzjvFY0e/bsYNVVVw3jrovC4qK/c9UQ8he+Dz/8cPEimX2exbirBpNuJNbzr3fv3mHc85QETnvcZ86cGeg3WbXS/N+lHmslAnWc8Dd4bRcxwWuvvVbyt2u7pQnXqZvYtmlryTJZnmG78An3f/vttw90PlxcbLPaYKmllgqXu+WWW4oXSeQ439y4n3nmmeG22q59AtssvGR7lej036etttqq5PWsz0hT3OM6/iR1XZn170Le949EYN6/Aex/kwRU88+fiJ144okV3/v9998X1MpTLYxo0Xv9elQLsFzRCZlvvrXIIosEtk+pcou5eTrZP+qoowruFvv110oE6oTAL6t1VCp//etfw+Vsv3GVFsvk/CzGvVKg1Jzd1zrwSW99P/KYCMxy3H2tACWf8toEtNLfQFxx10Wljt36+9EFWXES0H++br74Y/Cpp57qZ5c8zs1xXisbMmRI+Dm6EKpU1EzZb49uauWlZDXu9cRPzYB9zNUlSaXvaj3rarRl0hp3OT7++OMFyXsfIz3WSgT+3//9XxhT1QYrV2wfsYHtJzBcLk+/83PmzAnU5Y0slSit1vJGTae9ve0zu4Ay7uO8Vt7cuL///vthJQNtr+3buWBbo09sv4fhPo0cOTL6Uqan0xb3uI4/cV9XZvpLwM6FAiQCQwomEKgtoDuG/mTg7bffrvqGc845J1y2+CTMdtjrXlOipdoJt+1YPlxHpWZjSjKqeZffLiVx9IPgP6NWIlAnNf69ttP4ivukWot+OdVWzFPJYtzLxU/9yiy33HIuztpnNRH3MbeDx5R7S6bnZTnuagKl2KobAkqhQFxx100e//ejk/1qRX3Katlf//rXJTXI9b65Pc5rHb6JsmqA2wGkNKtsUcJRvyn6bvTt27fsMlmcmdW414qVju0+Ya3vRj21Umuts5FeT2PclaBTSxBfo0/HBjuQT0Frk1qJwJ133jk8/pSrDehj9Je//CVcTk0U81KeeeaZcL8POOCAqrutG/M+aajfzmiJ8zg/t3GPJiz32GOP6GaWTH/44Yfh/utcLy8lbXGP6/jjr/niuK7My3eB/QyC+e2PCwUBBOoUsAda06VLFzeSmEbTrVbsBV34su1EOJy2CURj7zy651tuuWXVkYU1cqM9EXTL2n5CwnVEJ2z/fmbq1Klulm3SZR566CFjmwYYW9PHzfPvj74nOq0RxcaPH2+0/t/+9rfRlwqmNcqlL9F98/Oy/JjFuJeLl0bGe/fdd81iiy1mbF8j4Xev3LJ5mJfVuNtaA0YjhKtssMEGeQhlk/YxjrjrA+0FR/i5tgZmOF1uwvbX5WZr9HlbK7dkkbk9ztt+oMzLL7/s1rvLLrsYe4Oo5DP8DI1Ub5sruZEmL7/8cj87849ZjHs9QTv44IONTf66RW3C2ug8Ik8ljXHXaO72BrIqa7hQ2IS8GTt2rLHJ+TA0tc7twgXtRPT8LTpf0xrp1hfbjY2fzPyjTLp37246depU83fQ3mA3NgHoTD7++GM36roHivM4P7dxtwlfv1mma9eu4XS5Ce2P/1t/5JFHwu9auWWzNC9tcY/j+BP3dWWW4s2+VBf4KVNQfRleRQCB/wnYAUHqtlByzRdbE8NPGluzI5y2/QeG0+UmllhiCaOkmy7cJ0yYUG4RN08XdbbDd2M7gjZ25N+Ky5V7wXYkbLR90W0st9yDDz4Yzt5pp53C6TxMZDHuxXFTItg2D3OzL7nkkvAEsXi5PD3Patxtn0dhGDfccMNwWskAW0vArLjiisb2HxTOz9tEHHGXmT/W6yKy1vE1eoGvY325xOHcHOeVRPClW7dufpLHiEAW4x7ZvbKTOu4/8cQT7jXbRNTYEWbLLpflmWmNu8x1HLBNfM1mm23W5BCsv/76RjcQVGz3A0bxLS62Gxtj+4gNZ9c6Jw0XzMCEbMsdZ8vtmh0MxCjZomJrbxs78Iqb1n9xH+e1zubGfdKkSXq7K9rOWkW/9bq+UHJz8uTJxvYhW+stDf962uIex/HHfwcVnFp/w/VeVzZ8oNmBugSoEVgXEwsh0DSBr776ytWw8+/aeOON/aR58803w2nb/CqcrjThax5qnfrBLi62A3p3gmKbAzc5CVi8rkrP7ejBxjZNdi/bUcbMFltsUWnRXM9v1LjbwV+M7TPMxW7fffc1+k5R6hdotLhHE4Grrbaaq0GsY5FuIqyyyirGNhM0OmbZpme5qSVQf7R/XrJa3LWULqxUdMy0g0u56Ur/+eO8XrdNNUsWm9vj/CuvvBKu018oqGa6EkEDBgxwNWJ23313YwcPCLc7fAMTBQKNFPeCDS96ov2wg1C4uUr82yaORklrSnmBeRl3xUEX9/fff3+zkoDaA/2m22aCbmfswEBGrT+iRbX/bL/QYU1h1Y5T8pBSKjBixIjwtzB6Pq8l4zzOz23cozW9q9UC9XvoWyfpue0uws/m8X8C8yru9YBXO/7EfV1Zz/awTDYEqBGYjTiyFykTsANruJo12izb/4OJJvyiP852JLKaW667N7589tlnJTW1Onfu7F+O7VEnB7aTYXeCc/PNN5tx48a5ddvBS8yoUaNqXtTGtiENtqJGjLuaouy///5Gd7xt/4BhwrfB6H/RzW20uEcTgbvttltJszE7uqx5/vnn3b/bbrvN6I71Cius8Isap/HDq8XdjrIcNrlrznG+eH/n9jivpr6+6GJRx3fbh5j7u/fz7ejw5tZbb3WJ4b///e+mX79+/iUeIwKNFPfIZpdMDh061KgpuooSzT5BXLIgM5zAvIy7mv2Wq8HXlFDouKPafuoKQOeOvXr1cs1F1QWMan/bfp/NlClT3Cr1Wf/+97+bsvrcLKu/EXW3o6JEnZrS+xL3cX5u477mmmv6TTO6+aObO5WKHSTM6CawL//973/9JI9WYF7GvR7wasefuK8r69kelsmGAInAbMSRvUiRgC6azz//fLdFqmFjR+Ut2DolXHxp1aqVn6z4GF1m5syZFZeL84U777zT1RKJrlNJIt1Rzlv/gFGDatONGnd9V9U0TCeg2of27dtX201eKxJoxLj7xL52RSeQthN6o+aidrRQ13fgmDFjjG4AqH8qXSwqYaSmpepGgPKTQKPFXbUJfFGfVgceeKDRhaBuNPm+YRVj1QrRxW3//v3dRaIuPig/CzRa3H/e8sIpnUv4Wv469p900kmFC/CsQKBR477pppsaNRe1g8K5Y/iTTz5p9C9a7MjgRol/aoNGVX6a1vl6jx49whsmqkEZbaadtvP56A0j1fDV9qrP53JFNwKi/UPaweLKLZbLeWmLe6Mef3L55WmwnaYNQIMFjM1Nt4D6YdEFlC8XXHCBq2Xln+sxekG28MILR18qOx3ti2ReJQLVF4o+d9lllw0HjNAgEiuvvLKxI8yFTSTKbnAOZzZq3FUD6JRTTnER0wkjfYc17cvbiHHX8cfXAtHeqm9RdTA+bNgws99++7nn6ivy8ccfN+rEWkV91mkQAcpPAo0adx8/xVl9g51xxhmu1oNqDemfaoKfdtppYf+QduR7V6vEvy/vj40Y90ox02BQvingrrvu6gZMqLRs3uc3ctxvvPFGs/baa7skoOKoZJ+6f4g2IVUXEGq5YkeLznuoC/ZfN0p69uzpBtPTC74bjehCaTufV7NlP/CUBgRTa49yNf00OIi6gYiWaFIwOj9v02mLeyMff/L23WnE/SUR2IhRY5tTKaA7Nn/4wx/cBZY2UAmWgw46qGRbo31F6WKsVokuU0/isNb66nl90KBBrlaIkn+qMaSRI1VTTD+QujiMJjvrWV+Wl2nUuCuW6g9wzpw5Zq211io5KcxyzOLYt0aNu2r1aWRx/U1fffXVZsiQIWGyP+qiEQc1aqUvSgT6kUX9vDw+Nmrco6OB6jdFx3HVAosOCqPpU0891d3sUWy13NFHH53HMJfsc6PGvWRH7Ax1B6HaQL5QG9BLlD42ctx1jNdvvAaAat26tav1p8SV+hNTElg3hJToUtFvgvp+nj59eilCDufIZ5tttnE3xLT76sNVN0uiLXQ0P43n8/rd9hUIRo8e7QaqOv300123ProBoNrgShbq+K7a/r7QGuSnfhLTFPdGPv747xWP6RYgEZju+LB1DSKgH1n9uCqpoqKLqcGDB5fdenXE74v64qpVosu0a9eu1uKxvK4TAn+BqO3t27eva07imwZq8BD1IZb30shxVyfxGpRAJ7LXXXedmVdJ5ix8Zxo57rpAUM1P/U2rtkC1oppCvvN4HdtUczDPpZHjHv3d0WiSqgFcqajWd4cOHdzLTz31VEHzsUrvyfL8Ro57ubiof8ipU6e6lzRatW8aXm7ZPM9r5LirFthxxx3nWm+o6fe9997r/uaVEPRlpZVWcsmhQw891M1SP6IadC7vRYN/qJsMdaGgoj6+1UVGtK9vbxQ9rkbP1f3rxY/RZZI6n9fIv7fccktY61M39FXTW7/n+t1XcknfA40orX6/fUlqe/z60/6Ytrg38vEn7bFm+34WIBH4swVTCDRZQHfUVOtPP7IqfuQ9/9zNLPpP/Qb6Eu3g1c8rfowuE31v8XJJP1fzEjUj9EXJwLyWRo+7LgrUFFRl4MCB7gRXFw7F/6K1iPQ99K9r//NYGj3uzYmZEgW+REee9fPy8NicuPvROuUTPYZX8oouk8RxPro96uPK3+gptz26SPQDR3z77bcFTcnLLZ/VeVmIe7nYjBw5Mpy9zz77hNNM/CSQhbjr5p7vv07nqKrtV6mon+COHTu6l2+44YZwoLtKy2d5vkZqVr+KfiTgDTbYwCUEyyUB5RA9rkaP4ZWMosskcZz3n6sBYvR7rUGAogMOKims/iIfffRRVytQ3QD5kudEYJri3pzjT/S7FP2O+dgWP0aXib63eDmeZ1+ARGD2Y8weJiSgA+mOO+5orrzySvcJbdq0cXdXBwwYUPUT1c+eL7pTV6v4ZVq2bBne4av1nqRe33LLLcNVR4erD2fmYCILcddgML6oieCiiy5a9p9GjPVFfc/45VRLKG8lC3FvTszUJMoX36eYf56Hx+bGXU3I/MW1P4ZX84ou4/tmrLZ8U19Tf6++VLqo9a/rUf2I+eIviv3zPDxmJe7FsdIAQP64rqTA3nvvXbxIrp9nJe4TJ04M46imjtWKzl0333xzt4j6ictrze/bb7/d1Zb/+OOPnYXO79VXbrVR39N2nI/GWQP7aSRo9RU4bdo019ehbubqRrCvCehHDdaNIdUQzWNJU9ybe/xp5OvKPH7n0rTPJALTFA22pWEEdEGsO6wPPvig22b94OqEIdrfRqWdUX9svtS6wFJzPP9DrdHA4m6+qb6C1BxE/cM88MADfrMqPkZPiDSyZN5KVuKet7jN7f5mKe662zxjxgzz8ssvm08//bQmjT/+aEF1lp6nMjdxl5M/1uvk3l9cVvKL3ljxF2mVlm3O/OhokuoSoFaJjiCpGwB5KlmKe3Hc1KWHHxRCNZ+iif7iZfP2PEtxjx7b/Q2JavGMJoHUp2Deyj//+U+z++67u36wte9qLq0bpkqS1ippOs5X2tblllvO9RUYrf2l6wudB6iss846BbUbK60na/PTFPe5Of7476Di80tfV2btO5L1/WmZ9R1k/xCIW0B31LbbbrtwJDE1mb3nnntKRgeu9Llqaqd+utTk6oknnqi0mJs/ZswYt5yebLLJJlWXbc6L2gb9gOhRF3u6c6iah5XKq6++Gr7km46FMzI+kaW49+jRI+wDrFrYVHPE1w445JBDwvessMIK1d6WqdeyFHcFRgMDnHfeeS5G6sJA/ZlWKz7+WmaNNdaotmimXpvbuAtDx2zdZFHRsV6DSVUqTz75ZPhSEsf66Dp931fhB5aZiCYm66lBWGYVDTkra3EvDkK0Nnf0O1G8XN6eZy3unTp1CkOo88hoa47whchEdCT5PB3nRaA+85T4U21Z1ZL929/+VtANToSp7GSajvP6HmuUWY0Ar5s/agZcqdxxxx3hyOG+RmilZbM4P01xn9vjT5quK7P4Xcn0PtkDHwUBBJogYJvSBPag4P7ZmhuBvfPahHf/tKgdqS1cx4svvljx/QcffHC4nO3Yt+Jy5V6wTcHce22n7+VeDufZ0cPq/gybQAqXHTFiRLiOPExkLe71xCy6z7YWUT1vydwyUYMs/L3bWszh37BN5ge2VnDFmD3yyCPhsnaAicDWJqy4bNZeiCPu48aNC/10zK9UbK3LwA7a45bdcMMNKy1Wdn69x3m9ec011wy3x/aJVHZ9mqntnn/++d2ytkuAistl8YUsxj0aJ9tnWPgdsCOIRl/K9XSjxN2OBBzG7+KLL64YM1ubLVxO53jViu1LMLCtPdzydkC4wPYNXG3xTL1m+9EL7M1vt+865l199dVN3r95cZyvN+52VOhAMdQ1iq3xW3Vftt9++/A7Uu33oOpKGvTFtMU9juPPvLiubNBws9lVBHQHhIIAAnUK2Nod4Q+nbQ4c2CZ2db6zcDF7xy5cj5ILthlW4QL22ejRo8OLQ1vrsOoFe8mb7Yx6LxAvueSScFtWXHHFQCeF5Up0OducqOw2l3tfFuZlMe71xCV6cpLHRGAW4z5r1qxg8cUXD//mhwwZUvarYJuHFSSORo0aVXa5LM6MK+6ysZ3NO2tb0yS49tprS7hmzpwZdO/ePYyHHe2xZJlqM+o9zmsd0WO4LvxtR/Elq9b3w9YOCbcnTzd8shr3aJD12+1vZNqaYtGXcjvdSHFvSkJI53M+1rYmeNn46kbQAQccEC7Xp0+fsstldWbXrl3DfbejtDZ7N5M+ztcbd+1At27d3D7pN0c388qVM844I9xvO7BIuUUyPS9NcY/r+DMvrisz/aXI6c7Np/22PxQUBBCoIaARVNWPxqRJk9yS6i9r1VVXrfGun162d97MEUccUbCsmmfed999bp6a2doLcjdamfp2ufXWW81xxx1n1IeHvUvp+irZaaedCt5f64n6BJk+fbprzlmtfyodAtQpst8WNRUeOnSosT+Uxt5ZNOp0Ws9VjV5F22NPLmo2NXELZ+C/rMa9ntD07t3b3HTTTW5R9Stma4XV87ZMLJPluKsrA/Vnqr99/T2rWZRGj7YXjq4vO40oePTRR7vmRQqmjg/2xkQm4lprJ+KOux+N0FsPHjzYaKRWm8Azzz33nGuq7buI6NKli1HTTcWk3lLvcV7r0zZstdVWYZcU+ns+5phj3HfB1hw3akJ45JFHmpdeesl9vJqLqe/bpmxPvdudtuWyHHdvbZPOYZ9nagJpaw+Fz/0yeXtstLhfccUVpl+/fi5MtkagOeywwyqGTMeVrbfe2qgvaBW9T6PIatRwFf2dn3jiie5vXM/V5Yf6jIv2I6f5WS3XXHONsYlPt3s6xqkZrf4u6im25mDB4H1JH+ebEnf1Xa59Udw1YvAFF1xgevXqZWytczcCss7pLr30Urebiy22mPvNyVNz8DTFPe7jT9LXlfX8bbBMgwnYE0MKAgjUIWATZeEdNPtn3qTpvn37lnyCmhTbvgYL1uObh0XXb3/ES95bz4ym1BSxicLAJjkLtsWeGIVNDPz22BPEIG/NibIc91rfozzXCMx63M8+++zAjhRY8DdvByMqeK6/e3sDI7A3JGp9VTLzetxxF4wduTGwnc4X2BYf6+0ovYGOw00tTTnOa912kKdAtX78Md0/Fm+Pfg/sSMZN3ZyGXT7rcVdgbD9wYdzt4BANG6s4N7zR4t6UmmFyGj58eNC6desw7vp719+6zu/8374e7aijgR1IJk7a1K/L3ngpMIh61Joud2xM8jjf1Ljbvn8L9k21A32TYb9vtl/woFrXRKkPYDM3ME1xj/v4k/R1ZTPJeVuKBeq/7WyPHBQE8iwQHSgjDgfdibv33nvdHVlNq6gGoC/q6Fe1cFQzJ+mi2iBjx451dw7btWvnPk53E3W3SsWeOLqO7jVwwIEHHujm5eW/LMc9LzFszn5mPe4nnHCCsRcBrsaI95k9e7ab1IBBqv2sWg8XXXRR1QGE/Huz8hh33OWiWjiqMWL7/zM2+eqo/LFeta51jNfrOg4nXWxSwNXutheWrka7rwHjt6d9+/audujTTz/tai0mvT1pWX/W4y5njRbuiwY5oxiT9birtrf2UTXA/UBw+lv3tQQXWWQRdw46YcIEd3zK03ciOhBWHPudpuO8BgLTqMe+1ZLNQ4Tn8xqsUDVJVftz/fXXj2PXG2odaYp73MefNF1XNtSXIscbS9PgHAefXU+XwNSpU43tdNi0atXKqNmxRmr8JZpk6QTxrbfeck2CbX9RRqPPaXuUDKTEL5CWuMe/Z6yxmkCa4v7ll1+6Lg80cqSah6mrAh2HKPELqHmmmuRNmzbN2FpZrrm9v/kS/6fVXqPtE9YlhDVivGKvBLCShZR4BdIW93j3jrVVEkhT3HVjV6OBq7sXJQDVDYy6FvA3AyrtA/ObLpCWuOt83g5G5X7fday3Nc/d+Xzbtm2bvlO8o6ZAWuLuNzRN55l+m3hMlwCJwHTFg61BAAEEEEAAAQQQQAABBBBAAAEEEEAgEQGaBifCykoRQAABBBBAAAEEEEAAAQQQQAABBBBIlwCJwHTFg61BAAEEEEAAAQQQQAABBBBAAAEEEEAgEQESgYmwslIEEEAAAQQQQAABBBBAAAEEEEAAAQTSJUAiMF3xYGsQQAABBBBAAAEEEEAAAQQQQAABBBBIRIBEYCKsrBQBBBBAAAEEEEAAAQQQQAABBBBAAIF0CZAITFc82BoEEEAAAQQQQAABBBBAAAEEEEAAAQQSESARmAgrK0UAAQQQQAABBBBAAAEEEEAAAQQQQCBdAiQC0xUPtgYBBBBAAAEEEEAAAQQQQAABBBBAAIFEBEgEJsLKShFAAAEEEEAAAQQQQAABBBBAAAEEEEiXAInAdMWDrUEAAQQQQAABBBBAAAEEEEAAAQQQQCARARKBibCyUgQQQAABBBBAAAEEEEAAAQQQQAABBNIlQCIwXfFgaxBAAAEEEEAAAQQQQAABBBBAAAEEEEhEgERgIqysFAEEEEAAAQQQQAABBBBAAAEEEEAAgXQJkAhMVzzYGgQQQAABBBBAAAEEEEAAAQQQQAABBBIRIBGYCCsrRQABBBBAAAEEEEAAAQQQQAABBBBAIF0CJALTFQ+2BgEEEEAAAQQQQAABBBBAAAEEEEAAgUQESAQmwspKEUAAAQQQQAABBBBAAAEEEEAAAQQQSJcAicB0xYOtQQABBBBAAAEEEEAAAQQQQAABBBBAIBEBEoGJsLJSBBBAAAEEEEAAAQQQQAABBBBAAAEE0iVAIjBd8WBrEEAAAQQQQAABBBBAAAEEEEAAAQQQSESARGAirKwUAQQQQAABBBBAAAEEEEAAAQQQQACBdAmQCExXPNgaBBBAAAEEEEAAAQQQQAABBBBAAAEEEhEgEZgIKytFAAEEEEAAAQQQQAABBBBAAAEEEEAgXQIkAtMVD7YGAQQQQAABBBBAAAEEEEAAAQQQQACBRARIBCbCykoRQAABBBBAAAEEEEAAAQQQQAABBBBIlwCJwHTFg61BAAEEEEAAAQQQQAABBBBAAAEEEEAgEQESgYmwslIEEEAAAQQQQAABBBBAAAEEEEAAAQTSJUAiMF3xYGsQQAABBBBAAAEEEEAAAQQQQAABBBBIRIBEYCKsrBQBBBBAAAEEEEAAAQQQQAABBBBAAIF0CZAITFc82BoEEEAAAQQQQAABBBBAAAEEEEAAAQQSESARmAgrK0UAAQQQQAABBBBAAAEEEEAAAQQQQCBdAiQC0xUPtgYBBBBAAAEEEEAAAQQQQAABBBBAAIFEBEgEJsLKShFAAAEEEEAAAQQQQAABBBBAAAEEEEiXAInAdMWDrUEAAQQQQAABBBBAAAEEEEAAAQQQQCARARKBibCyUgQQQAABBBBAAAEEEEAAAQQQQAABBNIlQCIwXfFgaxBAAAEEEEAAAQQQQAABBBBAAAEEEEhEgERgIqysFAEEEEAAAQQQQAABBBBAAAEEEEAAgXQJkAhMVzzYGgQQQAABBBBAAAEEEEAAAQQQQAABBBIRIBGYCCsrRQABBBBAAAEEEEAAAQQQQAABBBBAIF0CJALTFQ+2BgEEEEAAAQQQQAABBBBAAAEEEEAAgUQESAQmwspKEUAAAQQQQAABBBBAAAEEEEAAAQQQSJcAicB0xYOtQQABBBBAAAEEEEAAAQQQQAABBBBAIBEBEoGJsLJSBBBAAAEEEEAAAQQQQAABBBBAAAEE0iVAIjBd8WBrEEAAAQQQQAABBBBAAAEEEEAAAQQQSESARGAirKwUAQQQQAABBBBAAAEEEEAAAQQQQACBdAmQCExXPNgaBBBAAAEEEEAAAQQQQAABBBBAAAEEEhEgEZgIKytFAAEEEEAAAQQQQAABBBBAAAEEEEAgXQIkAtMVD7YGAQQQQAABBBBAAAEEEEAAAQQQQACBRARIBCbCykoRQAABBBBAAAEEEEAAAQQQQAABBBBIlwCJwHTFg61BAAEEEEAAAQQQQAABBBBAAAEEEEAgEQESgYmwslIEEEAAAQQQQAABBBBAAAEEEEAAAQTSJUAiMF3xYGsQQAABBBBAAAEEEEAAAQQQQAABBBBIRIBEYCKsrBQBBBBAAAEEEEAAAQQQQAABBBBAAIF0CZAITFc82BoEEEAAAQQQQAABBBBAAAEEEEAAAQQSESARmAgrK0UAAQQQQAABBBBAAAEEEEAAAQQQQCBdAiQC0xUPtgYBBBBAAAEEEEAAAQQQQAABBBBAAIFEBEgEJsLKShFAAAEEEEAAAQQQQAABBBBAAAEEEEiXAInAdMWDrUEAAQQQQAABBBBAAAEEEEAAAQQQQCARgZaJrJWVIoAAAggggAACCDSswLPPPmuuuuoqt/0bbbSR6du3b9V9mTZtmjnrrLPcMptvvrnZb7/9SpZ/6aWXzMiRI40ex48fbxZaaCGz3nrruX/9+vUzSy+9dMl7imf88MMP5rrrrnPreP31143+zZw506yyyipmtdVWc+s65JBD3LqL3/vYY4+Zm266yc0+55xzzBdffGH0OHr0aNOmTRuz3XbbmYEDB5rll1+++K08RwABBBBAAAEEMiMwX2BLZvaGHUEAAQQQQAABBBCYa4GPPvrILLPMMub77783HTp0MO+//75ZYIEFKq538ODB5tRTT3Wvjxo1yvTs2TNc9scff3QJt9NOO83MmTMnnB+dWGKJJcxll11W8L7o65qeOHGi6dOnj3n++eeLXyp4vvrqq5trrrnGbLzxxgXzhw0bZg4//HA3T4nI3//+9+add94pWOaKK64wBx10UME8niCAAAIIIIAAAlkSIBGYpWiyLwgggAACCCCAQEwCvXr1MnfccYdb21133WV23nnnimteddVVzeTJk40Seu+9915B0nCnnXYy99xzj3tvq1atTO/evV3NvdmzZ5sxY8YYJQ6VcFQZOnSoOeaYY9x09L8XXnjBqKbht99+62ZvtdVWZttttzVLLbWU+fzzz81rr71mbrzxxvB11Q5U4nD++X/uBSeaCOzevbt58MEH3bpatGhhVNOwdevW5oMPPjBt27aNfjTTCCCAAAIIIIBApgRoGpypcLIzCCCAAAIIIIBAPAIHHnhgmAhUc9xKicCnn37aJQH1qfvuu29BElBNcX0ScN111zW33HKLUdIwWp544gmzxx57GNVCPOWUU8xee+1lOnbsGF3EJQh9EnD48OHm0EMPLXhdT04//XTTpUsXM2PGDPPGG2+4z620zUoCrrzyyubCCy90CUXVEHz77bdJApaoMgMBBBBAAAEEsibw823SrO0Z+4MAAggggAACCCDQbIEdd9zRLLnkku79d955p/nqq6/KrkvNcH054IAD/KTru2/QoEHuuWoC3nzzzSVJQL24xRZbmGuvvdYt980335jjjz/eTfv/1CxZfQuqKNFXLgmo19S331FHHaVJV9QXYaWimoJXX321UW1F9VWoZsR77rlnpcWZjwACCCCAAAIIZEaARGBmQsmOIIAAAggggAAC8QmoT0DV8FOZNWuWufXWW0tWrlp6SvCpqMaf/vmi5sTvvvuue/rHP/7RDebhXyt+1EAdnTp1crPVxPe7774LF1lkkUXM/fff7/oQPO+888L55SbUJNgXJRUrlQ022MBsttlmlV5mPgIIIIAAAgggkFkBEoGZDS07hgACCCCAAAIIzJ2Amgf7oubBxUXJPvXRpxKtDajnap7rSz1JNz+4h/rrmzp1qn+r+dWvfmW23npro5GFu3btGs6PTqiPwWeeecbce++94Wzf72A4IzKhUYYpCCCAAAIIIIBAHgXoIzCPUWefEUAAAQQQQACBOgQ6d+5sVHtu7Nix5tFHH3UDgWg0YV98k95o7UH/2ptvvuknzXHHHWdOPPHE8Hm5iS+//DKcrfdq9N9yRdsybtw4o2WUbNQ/DVQSrUWo9wVBUO7tbl5xP4UVF+QFBBBAAAEEEEAgYwIkAjMWUHYHAQQQQAABBBCIU0C1ApV8+/HHH93IvErqqXz88cdhDTz1J6gRg6NFyTlftGxTypQpU0oWV43Ec88910yYMKHkNc1QMnKllVYykyZNKvt6dOayyy4bfco0AggggAACCCCQGwESgbkJNTuKAAIIIIAAAgg0XWCfffYxAwcONOoPUMk4nwjUiMBz5sxxK+zTp0/Jitu0aRPOO+2008ziiy8ePq81UdyU+MwzzzQnn3xy+LYWLVoYNe9dZ511wr4JNeiIRiDeZZdd3HLzzTdfuHzxhAYLoSCAAAIIIIAAAnkUIBGYx6izzwgggAACCCCAQJ0Ciy66qOnZs6cbFGT8+PGuKa4G5VAiUKVDhw5m5513LlmblnnooYfcfPX/16NHj5Jl6pnx8MMPh0nAli1bmrPPPtv079/ftGvXruTt0ebFqsFIQQABBBBAAAEEECgU4HZooQfPEEAAAQQQQAABBIoEooOGjBo1yjULfvbZZ91SqjGoZrnFJTqC74svvlj8cslz9fv33HPPmQ8//LCgfz8NSOKLahaqRmK5JKCWmTZtml/UVBssJFyICQQQQAABBBBAIGcCJAJzFnB2FwEEEEAAAQQQaKpA9+7djR8k5Pbbbzd333236zNQ6ykeLdivu0uXLn7SDB8+3Hz11Vfh8+KJWbNmuRqDes/SSy9tJk6cGC6i5KAv22yzjZ8sedRgISNHjgzna/RhCgIIIIAAAggggEChAInAQg+eIYAAAggggAACCBQJqE++/fff381VYm7EiBFuWn30rb/++kVL//R0k002MXvssYd78v7775tjjz3WVErODRo0yNUE1MLbbrutWWuttX5aif3fJyA14+WXXw7nRye03t69e7tBTfz82bNn+0keEUAAAQQQQAABBP4nQCKQrwICCCCAAAIIIIBATQFf8y8IAuObBZcbJCS6oiFDhpjWrVu7WZdffrnZcsstzWOPPWbUl5+Sd6+88opR0+KLL77YLaNlzzrrrOgqzNZbbx0+P/74481FF11kpk6d6uZ98sknRjUUe/XqZW677bZwOU3oNQoCCCCAAAIIIIBAocB89mQuKJzFMwQQQAABBBBAAAEESgU233xz89RTT7kXNHDH9OnTzVJLLVW6YGTOI488YtTHYLT/Po3oq1qG0X781M/gnXfeaXbYYYfIu41rgqxkoEYEjpaOHTuaGTNmhP0JdurUyVx66aVm9913d7ULVZNQn+lHCB42bJg5/PDD3SqUlOzbt290dUwjgAACCCCAAAK5EKBGYC7CzE4igAACCCCAAAJzLxAdNESjANdKAuoTu3XrZiZMmGAGDBhgNAKxiu5D+ySgb3b86quvliQBtawSeeqT8IQTTjALL7ywZrmi5sZaj/oU1EjCGmxEiUr1Z6jy3nvvudqH7gn/IYAAAggggAACCDgBagTyRUAAAQQQQAABBBCoS+D66683++23n1tWTXF33XXXut4XXUgJPCUGv/nmG7PSSiu5f23bto0uUnH666+/NpMnTzZTpkwxiy22mOncubPp0KFDxeV5AQEEEEAAAQQQQKBQgERgoQfPEEAAAQQQQAABBCoIqLbdQw895GoCvvvuu0bNeSkIIIAAAggggAACjSNA0+DGiRVbigACCCCAAAII/GICr732mnn44Yfd52vgEJKAv1go+GAEEEAAAQQQQKDZAtQIbDYdb0QAAQQQQAABBLIr8Pnnn7vRfdu3b2/GjBlj+vXr5wbfWHDBBc1bb71lNBgHBQEEEEAAAQQQQKCxBFo21uaytQgggAACCCCAAALzQkD9+G2xxRYlH6WBOUgClrAwAwEEEEAAAQQQaAgBmgY3RJjYSAQQQAABBBBAYN4KLL/88iUf2L9/f3PssceWzGcGAggggAACCCCAQGMI0DS4MeLEViKAAAIIIIAAAvNU4IcffjCq/Td+/Hg3su8OO+zgagi2aNFinm4HH4YAAggggAACCCAQnwCJwPgsWRMCCCCAAAIIIIAAAggggAACCCCAAAKpFaBpcGpDw4YhgAACCCCAAAIIIIAAAggggAACCCAQnwCJwPgsWRMCCCCAAAIIIIAAAggggAACCCCAAAKpFSARmNrQsGEIIIAAAggggAACCCCAAAIIIIAAAgjEJ0AiMD5L1oQAAggggAACCCCAAAIIIIAAAggggEBqBUgEpjY0bBgCCCCAAAIIIIAAAggggAACCCCAAALxCZAIjM+SNSGAAAIIIIAAAggggAACCCCAAAIIIJBaARKBqQ0NG4YAAggggAACCCCAAAIIIIAAAggggEB8AiQC47NkTQgggAACCCCAAAIIIIAAAggggAACCKRWgERgakPDhiGAAAIIIIAAAggggAACCCCAAAIIIBCfAInA+CxZEwIIIIAAAggggAACCCCAAAIIIIAAAqkVIBGY2tCwYQgggAACCCCAAAIIIIAAAggggAACCMQnQCIwPkvWhAACCCCAAAIIIIAAAggggAACCCCAQGoFSASmNjRsGAIIIIAAAggggAACCCCAAAIIIIAAAvEJkAiMz5I1IYAAAggggAACCCCAAAIIIIAAAgggkFoBEoGpDQ0bhgACCCCAAAIIIIAAAggggAACCCCAQHwCJALjs2RNCCCAAAIIIIAAAggggAACCCCAAAIIpFaARGBqQ8OGIYAAAggggAACCCCAAAIIIIAAAgggEJ8AicD4LFkTAggggAACCCCAAAIIIIAAAggggAACqRUgEZja0LBhCCCAAAIIIIAAAggggAACCCCAAAIIxCdAIjA+S9aEAAIIIIAAAggggAACCCCAAAIIIIBAagVIBKY2NGwYAggggAACCCCAAAIIIIAAAggggAAC8QmQCIzPkjUhgAACCCCAAAIIIIAAAggggAACCCCQWgESgakNDRuGAAIIIIAAAggggAACCCCAAAIIIIBAfAIkAuOzZE0IIIAAAggggAACCCCAAAIIIIAAAgikVoBEYGpDw4YhgAACCCCAAAIIIIAAAggggAACCCAQnwCJwPgsWRMCCCCAAAIIIIAAAggggAACCCCAAAKpFSARmNrQsGEIIIAAAggggAACCCCAAAIIIIAAAgjEJ0AiMD5L1oQAAggggAACCCCAAAIIIIAAAggggEBqBUgEpjY0bBgCCCCAAAIIIIAAAggggAACCCCAAALxCZAIjM+SNSGAAAIIIIAAAggggAACCCCAAAIIIJBaARKBqQ0NG4YAAggggAACCCCAAAIIIIAAAggggEB8AiQC47NkTQgggAACCCCAAAIIIIAAAggggAACCKRWgERgakPDhiGAAAIIIIAAAggggAACCCCAAAIIIBCfAInA+CxZEwIIIIAAAggggAACCCCAAAIIIIAAAqkVIBGY2tCwYQgggAACCCCAAAIIIIAAAggggAACCMQnQCIwPkvWhAACCCCAAAIIIIAAAggggAACCCCAQGoFSASmNjRsGAIIIIAAAggggAACCCCAAAIIIIAAAvEJkAiMz5I1IYAAAggggAACCCCAAAIIIIAAAgggkFoBEoGpDQ0bhgACCCCAAAIIIIAAAggggAACCCCAQHwCJALjs2RNCCCAAAIIIIAAAggggAACCCCAAAIIpFaARGBqQ8OGIYAAAggggAACCCCAAAIIIIAAAgggEJ/A/wPoVtGZF6h+GwAAAABJRU5ErkJggg==" />
<!-- rnb-plot-end -->
<!-- rnb-source-begin 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 -->
```r
```r
sea_014 <- perc_world %>% filter(g_whoregion == \SEA\) %>% select(newrel_m014, newrel_f014) %>%
t()
colnames(sea_014) <- c(\2013\, \2014\, \2015\, \2016\, \2017\, \2018\, \2019\, \2020\)
sea_014 %<>% barplot(col = c(\skyblue\, \seagreen1\), beside = TRUE, ylim = c(0,150000), xlab = \year\, ylab = \notifications\) + title(\Sex disaggregated notifications for ages 0-14 years in SEA\)
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVtZXJpYygwKVxuIn0= -->
numeric(0)
<!-- rnb-output-end -->
<!-- rnb-plot-begin -->
<img 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/>
<!-- rnb-plot-end -->
<!-- rnb-source-begin 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 -->
```r
```r
wpr_014 <- perc_world %>% filter(g_whoregion == \WPR\) %>% select(newrel_m014, newrel_f014) %>%
t()
colnames(wpr_014) <- c(\2013\, \2014\, \2015\, \2016\, \2017\, \2018\, \2019\, \2020\)
wpr_014 %<>% barplot(col = c(\skyblue\, \seagreen1\), beside = TRUE, ylim = c(0,50000), xlab = \year\, ylab = \notifications\) + title(\Sex disaggregated notifications for ages 0-14 years in WPR\)
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVtZXJpYygwKVxuIn0= -->
numeric(0)
<!-- rnb-output-end -->
<!-- rnb-plot-begin -->
<img 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/>
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<!-- rnb-text-begin -->
Creating plot for 15 plus
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<!-- rnb-source-begin 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 -->
```r
afr_15plus <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m15plus, newrel_f15plus) %>%
t()
colnames(afr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_15plus <- afr_15plus[, -8]
afr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in AFR")numeric(0)
amr_15plus <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m15plus, newrel_f15plus) %>%
t()
colnames(amr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_15plus <- amr_15plus[, -8]
amr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,300000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15 plus years in AMR")numeric(0)
emr_15plus <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m15plus, newrel_f15plus) %>%
t()
colnames(emr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_15plus <- emr_15plus[, -8]
emr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,500000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in EMR")numeric(0)
eur_15plus <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m15plus, newrel_f15plus) %>%
t()
colnames(eur_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_15plus <- eur_15plus[, -8]
eur_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,200000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in EUR")numeric(0)
sea_15plus <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m15plus, newrel_f15plus) %>%
t()
colnames(sea_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_15plus <- sea_15plus[, -8]
sea_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,2000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in SEA")numeric(0)
wpr_15plus <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m15plus, newrel_f15plus) %>%
t()
colnames(wpr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_15plus <- wpr_15plus[, -8]
wpr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in WPR")numeric(0)
Plotting total notifications over time
```r
tot_world <- mutate(perc_world, \newrel_mtot\ = newrel_m014 + newrel_m15plus, \newrel_ftot\ = newrel_f014 + newrel_f15plus, \TOT\ = newrel_mtot + newrel_ftot)
afr_tot <- tot_world %>% filter(g_whoregion == \AFR\) %>% select(newrel_mtot, newrel_ftot) %>%
t()
colnames(afr_tot) <- c(\2013\, \2014\, \2015\, \2016\, \2017\, \2018\, \2019\, \2020\)
afr_tot %<>% barplot(col = c(\skyblue\, \seagreen1\), beside = TRUE, ylim = c(0,1000000), xlab = \year\, ylab = \notifications\) + title(\Sex disaggregated notifications for all ages years in AFR\)
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVtZXJpYygwKVxuIn0= -->
numeric(0)
<!-- rnb-output-end -->
<!-- rnb-plot-begin -->
<img 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<!-- rnb-plot-end -->
<!-- rnb-source-begin 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 -->
```r
```r
amr_tot <- tot_world %>% filter(g_whoregion == \AMR\) %>% select(newrel_mtot, newrel_ftot) %>%
t()
colnames(amr_tot) <- c(\2013\, \2014\, \2015\, \2016\, \2017\, \2018\, \2019\, \2020\)
amr_tot %<>% barplot(col = c(\skyblue\, \seagreen1\), beside = TRUE, ylim = c(0,200000), xlab = \year\, ylab = \notifications\) + title(\Sex disaggregated notifications for all ages years in AMR\)
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVtZXJpYygwKVxuIn0= -->
numeric(0)
<!-- rnb-output-end -->
<!-- rnb-plot-begin -->
<img 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BBAAAEEEEAAAQQQQAABBBBAAAEEkhdINBB48803yw033CDr16+PXdJOnToRCIytxgIIIIAAAggggAACCCCAAAIIIIAAAghEE0gsEPjoo4/Kt771rWhrJRUCCCCAAAIIIIAAAggggAACCCCAAAIIVFQgkUDgjh075MILL8wW/KijjpIRI0ZI9+7dZbfddpMWLVpk54WN7LfffmGzmI4AAggggAACCCCAAAIIIIAAAggggAACZQokEgjU/gBXr15ti3LKKafII488IrvuumuZRWNxBBBAAAEEEEAAAQQQQAABBBBAAAEEEEhKIJGnBr/00kvZ8lx55ZUEAbMajCCAAAIIIIAAAggggAACCCCAAAIIIFAbAokEAvXJwDpoE+B+/frVxpZRCgQQQAABBBBAAAEEEEAAAQQQQAABBBDICiQSCDz22GNthplMJttEOLsGRhBAAAEEEEAAAQQQQAABBBBAAAEEEECg6gKJBAIPOeQQad++vd2YP/zhD1XfKAqAAAIIIIAAAggggAACCCCAAAIIIIAAArkCiQQCNctrr73W5jx27Fj597//nbsW3iGAAAIIIIAAAggggAACCCCAAAIIIIBAVQUSCwRedtllokHAlStXyqGHHip33XWXvPLKK7Jp06aqbiArRwABBBBAAAEEEEAAAQQQQAABBBBAAAGRpiQQ1q1bJyNHjrRZtWnTRt57773se5245557yk477VRwVVdffbXoHwMCCCCAAAIIIIAAAggggAACCCCAAAIIJC+QSCBw8+bNMnny5NDSrV27NnSeN4Oag54ErwgggAACCCCAAAIIIIAAAggggAACCCQvkEggsGXLlrLffvuVVbq2bduWtTwLI4AAAggggAACCCCAAAIIIIAAAggggEC4QCKBwA4dOsiKFSvC18KcUAHtU3H16tXywQcf2L/WrVtLu3btRAOj+iRmfc+AAAIIIIAAAggggAACCCCAAAIIIIBAuQKJBALLLURzWn7Dhg0yadIk25R64cKFou/DhqamJunZs6ccc8wxctppp8mpp54qLVq0CEvOdAQQQAABBBBAAAEEEEAAAQQQQAABBEIFEntqcOgamGEFVq1aZR+g0qVLF7nkkktkzpw5BYOAutD27dtl3rx5Mn78eBsIPPLII2XGjBmIIoAAAggggAACCCCAAAIIIIAAAgggEFsgtRqB+oCQl19+WV599VV55ZVXZMuWLaJNiDt27Cj9+/eXgw8+OHZh63WBNWvWyEknnSQLFizIboLW7OvcubN069bNuujTlnfZZRcb/NOHr6xfv942t162bJm10wW1BuHgwYNl3Lhxcvnll2fzYgQBBBBAAAEEEEAAAQQQQAABBBBAAIFiAokHAjXgd/PNN8uNN94oGtAKG7R2m6YZNGhQWJKGmL5x40a7jV4Q8Oijj5ZRo0bJgAEDbACw2EZu27ZNXnjhBduceOLEiaLvr7jiCjnkkENsU+FiyzMfAQQQQAABBBBAAAEEEEAAAQQQQAABFUi0abAGu7RPu+9+97sFg4C64r///e+2ueuYMWP0bcMO06ZNs82AdQOHDRsmc+fOta9aOzLK0KpVK+nXr59MmDBBHn30UdH3OlxzzTWyY8eOKFmQBgEEEEAAAQQQQAABBBBAAAEEEEAAAUmsRqDWBPziF78or732mmXVgNXZZ58thx12mHTv3t0+/VabuerfY489JsuXL7fpbrrpJptm+PDhDbk7nn/+ebtdWgNSHxLSsmXpsVd9WMgtt9wil112mW1mvGTJEunRo0dDurFRCCCAAAIIIIAAAggggAACCCCAAALJCiQWCNRagP/4xz9s6fQJt7feemtokOqHP/yh/OxnP5Orr77a1hzUh2ecddZZsvvuuye7dTWQ23PPPWdLcfrpp2dr85VTrKFDh9pAoOaxePHiUONy1sGyCCCAAAIIIIAAAggggAACCCCAAAKNJ1B69TSfhT7d9rbbbrNTPv7xj8vDDz9cMEClD8W49NJLs8ts2LBBpkyZ4suxcUbfeustuzFdu3ZNZKPat2+fDShu2rQpkTzJBAEEEEAAAQQQQAABBBBAAAEEEECg8QUSCQTqk4G1abAOd9xxh+y8886R5L72ta9J3759bdqnnnoq0jL1lshrujtnzpxEiq5NjfWBITr06dMnkTzJBAEEEEAAAQQQQAABBBBAAAEEEECg8QUSCQTqgz900Jp+Rx11VCy1T33qUza99h3YiIMX6Jw6darMmjWrrE1cu3atXHnllTaPvffe2/a9WFaGLIwAAggggAACCCCAAAIIIIAAAggg0GwEEgkErlu3zoLtuuuukWsDesLt2rWzo9q8uBEHfSqyPjhl8+bNMmTIEPv0361bt8be1Pnz58vJJ58s+qrDiBEjYufBAggggAACCCCAAAIIIIAAAggggAACzVcgkYeFHHrooVZwzZo18uabb8qBBx4YWfTFF1+0aXv27Bl5mXpKqE2Db7zxRhk9erRowFQDeDrev39/6d27t63V17FjR2nTpo19srIGRDVouH79elmxYoW8/vrrMnv2bFm4cGF2szUgeP3112ffM4IAAggggAACCCCAAAIIIIAAAggggEAxgUQCgYcffnh2PfrQEO/BIdmJISPapPiPf/yjnduogUDduKuuukr0IR8jR44UfcCHPhxl+vTp9i+EJnTywIEDZfLkydKyZSKVOUPXwwwEEEAAAQQQQAABBBBAAAEEEEAAgcYSSCSa9JGPfEQ0QKXD7bffLvfcc09RpeXLl8vZZ59ta781NTVlly+6YJ0mOP/880X7QRw7dqx06tQp1lZo34varPjxxx+XmTNnivYP+P/Z+xtwq6p6Ufz/8rKDrQgEIqCJcEg0lLcIyag/j3HhcgGh4uTh1L0lN1M6WCJIB7i3+y8N0hKPlRX0Il281JGy8KpxezpZeuRFTh5I8CXAgEASUl4FNrCFX2N29gqBzd6LvTasvfZnPM9kzTXnmGN852fCBr+OMYdCgAABAgQIECBAgAABAgQIECBAIB+BgowITB3ee++9kUb1pRVtb7jhhvjOd76TTYF9xzveEV27ds2mvb788suxcePG+PGPfxxz5syJqnflpffolfKIwKoH0qFDh5g5c2a2JYfly5fHunXrsmnAadpwGimY3ifYqlWraN26daRpxWm0ZZ8+fbJjVe34JECAAAECBAgQIECAAAECBAgQIJCvQMESgZdddlk2GnDSpElx8ODBePrpp7MRf1UBpVF/J1sQZMCAAfG5z32uqlqj+UzJ0bQpBAgQIECAAAECBAgQIECAAAECBM6EQEGmBlcFmhbCSIt/9OvXr+pQ7vP4JGAa9XbXXXfFU089lY2Cy1W0Q4AAAQIECBAgQIAAAQIECBAgQIBAwQUKNiKwKrIrrrgiGw3405/+NJ577rl44YUXsm3Xrl3ZVNdLL700evToER/96EfjwgsvrLqs0X5u3bo1duzYEfv378+2li1bRps2bbKpwWmBkfRdIUCAAAECBAgQIECAAAECBAgQIFBXgYInAlNA6T131113XV1jK8nr03sA58+fn638u2bNmuy9gNXdaJpOnd6dOHDgwBg1alSMGDEimjRpUl11xwkQIECAAAECBAgQIECAAAECBAhUK1DQqcHV9uJEbNu2LSZOnBgXXXRR3HzzzbFs2bJTJgETWZpOvXLlymxhlZQI7N27dzz22GM0CRAgQIAAAQIECBAgQIAAAQIECOQtUC8jAvOOosQv2LlzZwwdOjRWr16du9M0sq9z587RpUuXSKsJl5eXR4sWLbLkX0VFRbaS8ObNm2PTpk3Z4ivpwjSCcPTo0TF79uxIi7IoBAgQIECAAAECBAgQIECAAAECBGorkFcicN68eXHrrbdmbffs2TOWLl2a7W/fvj17719tOz1ZvWnTpkXaSq3s27cvRo4cmUsCplWSJ0+eHEOGDMkSgDXd7+HDh2PFihXZdOLkn76nZ5Des5imCisECBAgQIAAAQIECBAgQIAAAQIEaiOQVyLw0KFDsXv37qzdPXv25No/evRo7njuYJ47Bw8ezPOKhlF94cKF2TTgFO24ceOydwM2bVr7GdnpfYuDBg3KtjFjxsQHPvCBLBmYkqbDhw+PfNpqGGKiJECAAAECBAgQIECAAAECBAgQqA+BvBKB5513XnTt2jWLI73rrqo0a9Ysd7zqWL6fbdu2zfeSBlG/atRker9fWiSkLom7NALw7rvvjltuuSUbYbhhw4ZsJeYGASFIAgQIECBAgAABAgQIECBAgACBsyqQVyLwIx/5SKTt+HL++edHSkopJwosWbIkO3jttddmqymfWCO/I2PHjs0SgemqtWvXSgTmx6c2AQIECBAgQIAAAQIECBAgQKDRCtR+jmqjJarbjW/ZsiVr4OKLL65bQ/9xdfv27XMJxQMHDhSkTY0QIECAAAECBAgQIECAAAECBAiUvkBBEoFpAYtf//rX2ZbeI5hP+dGPfhRf+MIXYtGiRflc1mDqdu/ePYt12bJlBYk5TTVO3qn069evIG1qhAABAgQIECBAgAABAgQIECBAoPQFCpII3LFjR1xzzTXZlvbzKZ/4xCfi85//fPzsZz/L57IGU7d///5ZrA8++GA88cQTdYp7165dMWXKlKyNdu3aRbdu3erUnosJECBAgAABAgQIECBAgAABAgQaj0BBEoGny5WmtlZNb33ttddOt5mivm769OnZVN6KiopIq/7OnTs38h01mW5w1apVMWzYsOwzfZ8wYUL6UAgQIECAAAECBAgQIECAAAECBAjUSiCvxUJSiz/5yU9i69atb2p87969ue/z5s2LtLpwTeXgwYOxePHiqKyszKpeccUVNV3SIM+nqcGzZs2KqVOnxu7du7MEXtofPHhw9O3bNxvV17FjxygvL4+WLVtmHilpuGfPnti8eXOsX78+nnzyyVizZk3u/lNC8I477sh9t0OAAAECBAgQIECAAAECBAgQIECgJoG8E4FvvPFGfPrTn6623RkzZlR77lQnBg4ceKrTDfrcbbfdFmmRj4kTJ2YjIFPi9NFHH822fG9s+PDhsWDBgmja9KwO5sw3bPUJECBAgAABAgQIECBAgAABAgTOskDe2aQPf/jDMXTo0IKGPW3atBg5cmRB2yy2xsaPHx+bNm2KlCjt1KlTXuG1aNEim1b8yCOPZKMo0/sBFQIECBAgQIAAAQIECBAgQIAAAQL5COQ9IjA1/t3vfjcef/zxXD9pGustt9ySfb/33nujTZs2uXMn22nSpEk2DbZVq1aRpgR37dr1ZNVK7liHDh1i5syZ2bZx48ZYvnx5rFu3LpsGnKYNp5GCZWVlkVxat24daVpxz549o0+fPtmxkgNxQwQIECBAgAABAgQIECBAgAABAmdM4LQSgV26dInrr78+F+S2bdtyicC/+7u/y3vEW66hRrSTkp+NJQHaiB6rWyVAgAABAgQIECBAgAABAgQIFK3AaSUCj7+btDhIGgmYShrJphAgQIAAAQIECBAgQIAAAQIECBAgUFwCBUkEnnPOObkRgcV1e8UfTVqBeceOHbF///5sSysHp6nVKaGaFhhJ3xUCBAgQIECAAAECBAgQIECAAAECdRUoSCKwrkG88sor8eqrr8aVV15Z16aK/vr0HsD58+dnK/+uWbMmey9gdUE3b948evXqFWlF5VGjRsWIESMivV9RIUCAAAECBAgQIECAAAECBAgQIJCvQMETgS+//HI89NBDsX379jh06FAcOXLkTTG98cYbkbbKysrYtWtXbNmyJZYuXRr/83/+z5JOBKb3KN5+++3xwAMPnDL5dyxWMlq5cmW2zZkzJ/O58847S36F5WMN7BMgQIAAAQIECBAgQIAAAQIECBRGoKCJwJtvvjm+/e1vx+HDhwsTXYm0snPnzhg6dGisXr06d0dpZF/nzp0jLbySVhMuLy+PFi1aZAnSioqKbCXhzZs3x6ZNm+LgwYPZdWkE4ejRo2P27NkxadKkXFt2CBAgQIAAAQIECBAgQIAAAQIECNQkULBE4P/+3/87vvGNb9TU3wnnmzVrFldddVW8+93vPuFcKRzYt29fNoKvKgk4YMCAmDx5cgwZMiRLANZ0jympumLFimw68bx587Ik66233ho9evTIpgrXdL3zBAgQIECAAAECBAgQIECAAAECBJJAQRKBafpvSm5Vleuuuy6GDx8eHTt2jA9/+MPZIhhf+MIXsvfdpYUxUmIrTZE9cOBAXHPNNfGLX/yi6tKS+1y4cGEsW7Ysu69x48Zl7wZs2rRpre+zrKwsBg0alG1jxoyJD3zgA1kycNq0aZlxPm3VulMVCRAgQIAAAQIECBAgQIAAAQIESk6g9hmpU9x6msKaEnypTJgwIR588MEYP358NmLtve99b3Y8jYz74Ac/GJ/4xCdi7ty5sXjx4mjVqlX8y7/8S/zwhz/M6pTiL+n9h6n07t07G9VXl8RdWizk7rvvztpLIww3bNiQ7fuFAAECBAgQIECAAAECBAgQIECAQE0CBUkErl27NtdPGql2bEmj2VL55S9/eezhGDx4cDYSML0rL011TQuHlGJZsmRJdlvXXnttpNF9dS1jx47NNXGse+6gHQIECBAgQIAAAQIECBAgQIAAAQInEShIInD9+vVZ0+ecc05ccsklb+rmHe94R/b9ueeey1YLPvZkei9gr169Iq2om0YRlmJJqyKncvHFFxfk9tq3b59LKKap1QoBAgQIECBAgAABAgQIECBAgACB2ggUJBGYEoCpvPWtbz2hz7SoRSppJdyTjWBLIwNTqVpMI/tSQr907949u5uq9wTW9dbSVOOqVZn79etX1+ZcT4AAAQIECBAgQIAAAQIECBAg0EgECpIIvPzyyzOu7du3x9GjR99Ed+mll0aa/pvKb3/72zedS1/SiMBUSjUR2L9//+z+0ojHJ554Its/3V/S9OkpU6Zkl7dr1y66det2uk25jgABAgQIECBAgAABAgQIECBAoJEJFDQRmEaqPfnkk28iTKMFL7zwwuzYb37zmzedS1+eeuqp7FjVYiMnVGjgB6ZPn55N5U0jItOqv2mhlEOHDuV9V6tWrYphw4ZF+kwlLcqiECBAgAABAgQIECBAgAABAgQIEKitQPPaVjxVvTZt2kTXrl1j48aNMWnSpPjZz34WnTt3zl2SprC+/PLL2XsA/8f/+B+5KcRHjhzJ6qaKf/M3f5OrX0o7aWrwrFmzYurUqbF79+4sgZf205Tovn37ZqP6OnbsGOXl5dGyZcuorKzMplHv2bMn0mrM6f2LKbm6Zs2aHEtKCN5xxx2573YIECBAgAABAgQIECBAgAABAgQI1CRQkERg6uSrX/1qNuItjVhLC4R89KMfjW984xtZ/9dff308+uijkRbOGDVqVJYUa9u2bXbNq6++mtWpmiKcfSmxX2677bZIi3xMnDgx0gIfe/fuzTySSb5l+PDhsWDBgmjatCCDOfPtXn0CBAgQIECAAAECBAgQIECAAIEGKlCwbNLo0aPjYx/7WMaQRr49/PDDOZIPfvCDkd4VmEpa7CJ9v+aaa2LRokXZsdatW8dnPvOZbL9Ufxk/fnxs2rQpZsyYEZ06dcrrNlu0aJElWR955JFYvHhxpPcDKgQIECBAgAABAgQIECBAgAABAgTyESjYiMDU6be+9a1Ii2N8/etff9PU4DR67ec//3mk0WzHrxycpsSm9+ZdcMEF+cTdIOt26NAhZs6cmW1pGvXy5ctj3bp1kaYBp+RpGilYVlYWrVq1ipQcTdOKe/bsGX369MmOna2b/j//5/9kz+j4hWBOJ57f//732WV/+tOfTudy1xAgQIAAAQIECBAgQIAAAQIECJymQEETgWlhkDSy79Of/nSW4Do2prTCbRoN+MADD8Svf/3rSFOC07sDP/WpT2XJrmPrNob99E7FtDWEkqYwVy3qUqh40zRxhQABAgQIECBAgAABAgQIECBA4MwJFDQRWBV2kyZNokePHlVfc5/pPXlpMZG0KQ1H4P77749bbrkl0uIudS1f+9rXYuHChVkSuK5tuZ4AAQIECBAgQIAAAQIECBAgQKD2AvWSCEwLYqxYsSJbGff4UFavXp0tlJHeE3j55Zcff7rRfd+6dWvs2LEj9u/fn21p5eC0CnOaGpwSp+n72S5ppOfVV19dkDB+/OMfF6QdjRAgQIAAAQIECBAgQIAAAQIECOQnUNBEYGVlZUybNi2+/e1vZ++6e+21106I5plnnskWzEiLZgwZMiR+8IMfNIr3A1ZBpPcAzp8/P1v5d82aNdl7AavOHf/ZvHnzSKspDxw4MFttecSIEZFGWyoECBAgQIAAAQIECBAgQIAAAQIE8hUo2KrBaURbSlTNnj07S26lUW7pPYDHlw0bNuQO/fKXv8wWF3n++edzx0p1Z9u2bTFx4sS46KKL4uabb45ly5adMgmYHFJideXKlTFnzpwsEdi7d+947LHHSpXIfREgQIAAAQIECBAgQIAAAQIECNSjQMFGBN5zzz3xi1/8Igu1Xbt28fGPfzwbFXh87GkhkfT+wPTeuccffzzSohE33nhjwRejOL7fs/l9586dMXTo0EjToqtKGtnXuXPn6NKlS6TVhNPqyS1atMiSfxUVFdlKwps3b45NmzbFwYMHs8vSCMLRo0dnyVbvWayS9EmAAAECBAgQIECAAAECBAgQIFAbgYIkAtN017vvvjvr74orrojFixfHxRdffNL+zz///PjoRz8aH/nIR+Jzn/tczJw5M5YsWZItIHHddded9JqGfHDfvn0xcuTIXBJwwIABMXny5GxadEoA1lQOHz6cvW8xTSeeN29epO+33nprlkxNIzAVAgQIECBAgAABAgQIECBAgAABArURKMjU4GeffTZ2796d9fed73yn2iTgsQGlEXFf+MIX4sorr8wOp2nCpVjSCrlpGnAq48aNi+XLl2eftUkCpmvKyspi0KBBMXfu3Fi0aFFulGV6F2MhVvFNfSgECBAgQIAAAQIECBAgQIAAAQKlL1CQROD69eszqbZt2+a1umyzZs3iP/2n/5Rd+8ILL5Sk9tKlS7P7Su/3S6P6mjY9ffI0ArBq5GWaZnzs+xZLEs9NESBAgAABAgQIECBAgAABAgQIFEzg9LNSx4SQpgancjpJrjZt2mTX7tmzJ/sstV/StOdUrr322txovrrc49ixY3OXr127NrdvhwABAgQIECBAgAABAgQIECBAgMCpBAqSCEwLXqSSVgreuHFjtl/bX9KquKn06tWrtpc0qHppMZRUqntnYr430759+1xC8cCBA/lerj4BAgQIECBAgAABAgQIECBAgEAjFShIIrBfv3650YB33nlnrSmff/75+Jd/+Zesfpo6W4qle/fu2W1VvSewrveYphqnBUNSSe4KAQIECBAgQIAAAQIECBAgQIAAgdoIFCQRmEa7DR06NOsvLWrxla98JZesqi6I9E7ANM11//798Za3vCVbWbe6ug35eP/+/bPwH3zwwXjiiSfqdCu7du2KKVOmZG20a9cuunXrVqf2XEyAAAECBAgQIECAAAECBAgQINB4BAqSCExcX/ziF6NFixaZ3Gc/+9no0aNHfOlLX4q0am5aKTetLLx48eJIqwp/+MMfjiuuuCJefPHFrP7tt98ePXv2zPZL7Zfp06dnU3krKipizJgx2eq/hw4dyvs2V61aFcOGDYv0mcqECRPybsMFBAgQIECAAAECBAgQIECAAAECjVegeaFu/V3velfMmTMnbrjhhnjjjTeydwXOmDGjxubTSrhTp06tsV5DrZCmBs+aNSu7x927d2cJvHS/gwcPjr59+2aj+jp27Bjl5eXRsmXLqKysjJQ0TIunbN68OdKKzE8++WSsWbMmR5ASgnfccUfuux0CBAgQIECAAAECBAgQIECAAAECNQkULBGYOrr++usjvevvU5/6VKxYseKUfV9yySXx5S9/Oa677rpT1iuFk7fddlukRT4mTpwYaYGPtMryo48+mm353t/w4cNjwYIFuXcy5nu9+gQIECBAgAABAgQIECBAgAABAo1ToKCJwET4zne+M55++ulYt25dluhau3ZtbNu2LQ4ePBh/8zd/E29/+9uzbciQIdkIuMbCPn78+Bg1alTce++9cf/998crr7xS61tPU65TAjCNtkxtKAQIECBAgAABAgQIECBAgAABAgTyFSh4IrAqgEsvvTRuvfXWqq8+/yzQoUOHmDlzZrZt3Lgxe3diSpimacBp2nAaKVhWVhatWrWK1q1bHNguyAAAQABJREFUR5pWnN6d2KdPn+wYRAIECBAgQIAAAQIECBAgQIAAAQKnK1BvicDTDaixXNe1a9dIm0KAAAECBAgQIECAAAECBAgQIEDgTAhIBJ4J5VP0sXXr1tixY0fs378/29KCIW3atMlGBKb3CqbvCgECBAgQIECAAAECBAgQIECAAIG6CuSVCJw3b15uum+asrp06dKs/+3bt0ePHj3qFMu0adMibaVe0vTf+fPnZwt+pJWA0/fqSvPmzaNXr14xcODA7N2AaYXlJk2aVFfdcQIECBAgQIAAAQIECBAgQIAAAQLVCuSVCDx06FD2LrvUWnqvXVU5evRo7njVsXw/02IipVzSgim33357PPDAA6dM/h1rUFlZGStXrsy2OXPmxJVXXhl33nlnjBw58thq9gkQIECAAAECBAgQIECAAAECBAjUKJBXIvC8887LvdfuoosuyjXerFmz3PHcwTx32rZtm+cVDaf6zp07Y+jQobF69epc0GlkX+fOnaNLly7ZIiLl5eWRVgdOyb+Kioos0bp58+bYtGlTtuJyujCNIBw9enTMnj07Jk2alGvLDgECBAgQIECAAAECBAgQIECAAIGaBPJKBH7kIx+JtB1fzj///NiwYcPxh33/s8C+ffuyEXxVScABAwbE5MmTY8iQIVkCsCakw4cPx4oVK7LpxGlqdvqeVmNOU7HTVGGFAAECBAgQIECAAAECBAgQIECAQG0EmtamUlWdqVOnZu+su/HGG6sO+axBYOHChbFs2bKs1rhx42L58uWRPjt06FDDlX85XVZWFoMGDYq5c+fGokWLIn1PJb1P8ciRI3+p5FcCBAgQIECAAAECBAgQIECAAAECNQjklQj8/e9/n01Pfeqpp97U7GuvvRZ9+/bNtrSv/FWgakGV3r17Z6P6mjbNi/yvDf15L40AvPvuu7NjaYShUZhv4vGFAAECBAgQIECAAAECBAgQIEDgFAJ5ZaXSgheppHfepQVCqkp6r91vf/vbbEtTV5W/CixZsiT7cu211+ZG8/31bP57Y8eOzV20du3a3L4dAgQIECBAgAABAgQIECBAgAABAqcSyCsRWLWgxyuvvBJPP/30qdp17j8EtmzZku1dfPHFBTFp3759LqF44MCBgrSpEQIECBAgQIAAAQIECBAgQIAAgdIXyGuxkDT997HHHstUhg0bFmnr2LFjHJuQmjFjRpxzzjl5y6Vpr6W4+EX37t1j1apV2XsCb7rpprxdjr8gTTWuGnXZr1+/40/7ToAAAQIECBAgQIAAAQIECBAgQOCkAnklAidMmBDf//734+WXX469e/fGQw89dEKjaWXb0ylp5eFSTAT2798/SwQ++OCDMX78+Bg8ePDp8GTX7Nq1K6ZMmZLtt2vXLrp163babbmQAAECBAgQIECAAAECBAgQIECgcQnkNTX4bW97W/zmN7+J973vfY1LqQ53O3369Gwqb0VFRYwZMyZb/ffQoUN5t5hGFaYRmOkzlZSUVQgQIECAAAECBAgQIECAAAECBAjUViCvEYGp0U6dOsWTTz4Z6T2BafGQlOBKKwWPHDky6zNNHU6j1fItKclYiiVNDZ41a1ZMnTo1du/enSXw0n4aGZimWqdRfWl6dXl5ebRs2TLSwivJdM+ePbF58+ZYv3595r1mzZocT0oI3nHHHbnvdggQIECAAAECBAgQIECAAAECBAjUJJB3IrCqwZQQTFsqVasJp/13vvOduePpuxJx2223RVrkY+LEidn7FNO06kcffTTb8vUZPnx4LFiwIJo2zWswZ77dqE+AAAECBAgQIECAAAECBAgQIFBiAgXJJrVo0SJGjRqVbWlUm3KiQHo/4KZNmyItplKVQD2x1smPJN80rfiRRx6JxYsXn9aIy5O37CgBAgQIECBAgAABAgQIECBAgEBjETjtEYHHArVt2zZLUh17zP6JAh06dIiZM2dm28aNG2P58uWxbt26bBpwmjacRgqWlZVFq1atonXr1pGmFffs2TP69OmTHTuxRUcIECBAgAABAgQIECBAgAABAgQI1E6gIInA2nWl1rECXbt2jbQpBAgQIECAAAECBAgQIECAAAECBM6EQF5Tg+fNmxdp9F/a3vOe9+Ti2759e+541fl8P++8885ce3YIECBAgAABAgQIECBAgAABAgQIECisQF4jAg8dOpStfJtCSKvaVpWjR4/mjlcdy/fz4MGD+V6iPgECBAgQIECAAAECBAgQIECAAAECtRTIKxF43nnn5aazXnTRRbkumjVrljueO5jnThpBqBAgQIAAAQIECBAgQIAAAQIECBAgUD8CeSUCP/KRj0Taji/nn39+bNiw4fjDvhMgQIAAAQIECBAgQIAAAQIECBAgUCQCeb0jsEhiFgYBAgQIECBAgAABAgQIECBAgAABAnkK5DUisLq2Dx8+HEuWLMlOp0VE3vKWt1RX9YTjP/rRj+L555+PPn36xAc+8IETzjtAgAABAgQIECBAgAABAgQIECBAgEDdBQqSCNyxY0dcc801WTR//OMfo1OnTrWO7BOf+ETs3bs3PvnJT5ZkIrBDhw6RFlOpj/Lqq6/WR7PaJECAAAECBAgQIECAAAECBAgQKEGBgiQCT9flwIEDkbZUXnvttdNtpqivS0nSI0eOFHWMgiNAgAABAgQIECBAgAABAgQIECh9gbwTgT/5yU9i69atb5JJI/qqyrx58yKtLlxTOXjwYCxevDgqKyuzqldccUVNlzTI8//6r/8a48ePj7Vr1+biTyMm00rLCgECBAgQIECAAAECBAg0LoGj/zFQ5He/+13Mnz+/qG++efPmMXLkyGjTpk1Rxyk4AgRqL5B3IvCNN96IT3/609X2MGPGjGrPnerEwIEDT3W6wZ5L70xcvnx5DB8+PFasWJHdx3//7/89Zs6c2WDvSeAECBAgQIAAAQIECBAgcHoCr/zu2ezCX/3qV5G2Yi+33HJL3HvvvcUepvgIEKilQN6JwA9/+MMxdOjQ+MUvflHLLmquNm3atOz/MtRcs2HWeOtb35p5vfvd744XXngh7rzzzhgyZEi8//3vb5g3JGoCBAgQIECg3gQO7N6Rtf2Nb3wjHnvssXrrpxANp1kgs2bNivPPP78QzWmDQKMTeOPwoeye04CBr371q0V9/2lByL//+7+Ptm3bFnWcDSG4ykMVWZjn9ugQb333JUUb8r71r8bOpRtjz549RRujwAgQyF8g70Rg6uK73/1uPP7447ne0g+G9H8JUkn/p6CmYcNNmjSJli1bRqtWrSJNCe7atWt2bSn/0rp16/je974X733ve7N3Bv63//bfsqRgOq4QIECAAAECBKoEdmz+fbabZhSkrdjLoEGD4uMf/3ixhyk+AkUp8McXf5vFlQZZFHKgRX3d7LZt2+Lzn/98fTXf6Npt//63R+9vfbho73vzvKezRGDRBigwAgROS+C0EoFdunSJ66+/Ptdh+guhKhH4d3/3d3mtGpxrpBHsXH311dm06vR/+9J7Fu+777443anUjYDLLRIgQIAAgcYpcPRodt+X/MOgaPfebkVr8IdvL4vXfv1S7n3PRRtoAwnsyBt/eW/2b3/723jooYeKOury8vIYNmxYpHeHKXUTOFJ5OGug7VVd4q1XF+/IsF3PbImdT22I/fv31+2GXU2AAAECZ12gIH97p2khVe8MMMLt1M80vRvw//7f/xsbNmyIe+65Jz7zmc9kIyNPfZWzBAgQINAYBV5/dVt22zfffHMU+9+v7dq1ix/84AdxwQUXNMZHVS/3nJICF/39O+ul7UI0+uov12WJwEK0pY2IV/5jZNj3v//9SFuxl7lz58aNN95Y7GE2mPjOH9ojLv/iiKKN96WvPJ4lAos2QIERIECAQK0FCpIIPOecc3IjAqt63rVrVzz//PORVkJ68cUXI60S3KFDh+jYsWMMHjw4Lr300qqqjerz3HPPjR/+8IfxyCOPZPedEoK9evVqVAZutm4CFa//5R0dafWuFi1a1K2xer66W7du8c///M+R3imjECCQv0BVIvCll17K/+KzcEWaxjp69Oiz0LMuCTR8gap3xZ3Xu3Oce2mHor2hvav/GPvW/inSv/UVAgQIECBAoOEJFCQReOxtp4Tfl7/85ezF0RUVf3kJ6rHnq/Z79+6d1UnJjMZW0grJpbpKcmN7lmfjfg/tfz3rNi08U+xl1apVsXHjxujRo0exhyo+AkUtcMXXPxjtBhXvFNHnbn04djzxUhz9jymtRY0pOAJFLnDx+KvibyYNLtooX/jHR+KlL/+qaOMTGAECBAgQIHBqgYImAlevXh1jx46NdevWnbrXP5999tlnY9SoUZFWDP7Sl75UY30VCBB4s8CAR2+Ilhe1efPBIvr2mw/eHwc27pQYKKJnIpSGK3Du28+PNv3eVrQ3UNa2ZdHGJjACBAgQIECAAAECBP4qULBEYBoJmJaTr0oClpWVxd/+7d/GO97xjkjTA9MqwZs2bcq2hx9+OP7whz9kUdx5551ZnY997GN/jcoeAQI1CpzXs2Oc0619jfXOVoWmLQr24+Vs3YJ+CRAgQIAAAQIECBAgQIBASQkU7L/U/9f/+l/x3HPPZThppF9aPKR79+4nxfrKV74S3/72t+Ozn/1spOnD6SXoH/rQhyyacVItBwkQIECAAAECBAgQIECAAAECBAjUXaAgicDKysr46le/mkXzrne9Kx566KFTLg6QFjj49Kc/nS10cNNNN8XevXuzBTQ++clP1v2OtECAAIESFDiwe2d2V5/61KeKfvXYzp07x3e+850oLy8vwSfhlggQIECAAAECBAgQINBwBQqSCEwrA6epwal8/etfP2US8FiqG2+8MRsZ+Mwzz8TPf/7zkAg8Vsc+AQIE/iqwf9er2Zff/OY3fz1YxHsTJ06Mq6++uogjFBoBAgQIECBAgAABAgQan0BBEoFp4Y9U0ki/d77znXkpvuc974mUCEzvD1QIECBA4OQCVYuxXvmNP79G4bILTl6pCI6uueWn8fpz2+LIkSNFEI0QCBAgQIAAAQIECBAgQOBYgYIkAnfv3p21ec4559R6NGBVEG3a/GXV0zS9WCFAgACBUwu0veqSaPuui09d6SyeLWtrOvBZ5Nc1AQIECBAgQIAAAQIETinQ9JRna3ny8ssvz2ru3Lkzfv/739fyqr9US6MBU+nVq9dfDviVAAECBAgQIECAAAECBAgQIECAAIGCCxQkEdizZ89cYFWLhuQOnGInTSn+1a9+ldWQCDwFlFMECBAgQIAAAQIECBAgQIAAAQIE6ihQkETgBRdcEMOHD89C+drXvhbf+973agzrD3/4Q/zt3/5tVFRURPPmzXPX13ihCgQIECBAgAABAgQIECBAgAABAgQI5C1QkERg6vXee++NsrKyLIAbbrgh3v3ud8dDDz0Uzz//fOzfvz97cfzmzZvjX//1X+OWW26JSy+9NNatW5fVnz59uqnBmYRfCBAgQIAAAQIECBAgQIAAAQIECNSPQEEWC0mhXXbZZZFGA06aNCkOHjwYTz/9dDbiryrsNOrvZAuCDBgwID73uc9VVfNJgAABAgQIECBAgAABAgQIECBAgEA9CBRsRGCKbcKECZEW/+jXr98JoR6fBGzVqlXcdddd8dRTT+VGEp5wkQMECBAgQIAAAQIECBAgQIAAAQIECBREoGAjAquiueKKK7LRgD/96U/jueeeixdeeCHbdu3aFd27d8+mBPfo0SM++tGPxoUXXlh1mU8CBAgQIECAAAECBAgQIECAAAECBOpRoOCJwBRrelfgddddV49ha5oAAQIECBAgQIAAAQIECBAgQIAAgXwECjo1OJ+O1SVAgAABAgQIECBAgAABAgQIECBA4MwJSASeOWs9ESBAgAABAgQIECBAgAABAgQIEDhrAgWfGvz666/Hj370o/jd734X+/bty1YKPnr0aI03OGrUqEibQoAAAQIECBAgQIAAAQIECBAgQIBA4QUKmgj88pe/HDNnzow9e/bkHWmnTp0kAvNWcwEBAgQIECBAgAABAgQIECBAgACB2gkULBG4aNGi+Md//Mfa9aoWAQIECBAgQIAAAQIECBAgQIAAAQJnVKAgicAjR47EJz7xiVzg73znO2PChAnRrVu3OPfcc6NJkya5c9XtvO1tb6vulOMECBAgQIAAAQIECBAgQIAAAQIECNRRoCCJwPQ+wB07dmSh/Of//J/jJz/5SZxzzjl1DM3lBAgQIECAAAECBAgQIECAAAECBAgUSqAgqwb/+7//ey6eKVOmSALmNOwQIECAAAECBAgQIECAAAECBAgQKA6BgiQCKysrs7tJU4AHDRpUHHcmCgIECBAgQIAAAQIECBAgQIAAAQIEcgIFSQReffXVWYNHjx7NTRHO9WCHAAECBAgQIECAAAECBAgQIECAAIGzLlCQRGCPHj2iffv22c388pe/POs3JQACBAgQIECAAAECBAgQIECAAAECBN4sUJBEYGryc5/7XNbyjBkz4tVXX31zL74RIECAAAECBAgQIECAAAECBAgQIHBWBQqWCLzlllsiJQG3bt0al19+eXzzm9+MF198MQ4cOHBWb1DnBAgQIECAAAECBAgQIECAAAECBAhENC8Ewu7du2PixIlZU+Xl5fHaa6/lvqeDbdu2jWbNmp2yq89+9rORNoUAAQIECBAgQIAAAQIECBAgQIAAgcILFCQRWFFREQsWLKg2ul27dlV7ruqEkYNVEj4JECBAgAABAgQIECBAgAABAgQIFF6gIInApk2bxtve9rY6Rde6des6Xe9iAgQIECBAgAABAgQIECBAgAABAgSqFyhIIrBDhw6xefPm6ntxhgABAgQIECBAgAABAgQIECBAgACBsypQsMVCzupd6JwAAQIECBAgQIAAAQIECBAgQIAAgVMKFGRE4Cl7cJIAAQIECBAgQIAAAQIECBAg0IgFDh+syO7+6aefjv/6X/9rUUuk17/deOON8d73vreo4xTc6QlIBJ6eW8Gu2rp1a+zYsSP279+fbS1btow2bdpEemdi+/btI31XCBAgQIAAAQIECBAgQIAAgYYr8KeXXsiCT69VO9Viq8Vyh6+//rpEYLE8jALHIRFYYNCamtu7d2/Mnz8/+4O/Zs2aSN+rK82bN49evXrFwIEDY9SoUTFixIho0qRJddUdJ0CAAAECBAgQIECAAAECBIpQ4OjRI1lUba/qEl0/874ijPAvIe3+9y2x4Z4norKysmhjFFjdBCQC6+ZX66u3bdsWt99+ezzwwAOnTP4d22D6g7dy5cpsmzNnTlx55ZVx5513xsiRI4+tZp8AAQIECBAgQIAAAQIECBBoAALlXdrG2z7av2gjLWvdMksEFm2AAquzgERgnQlrbmDnzp0xdOjQWL16da5yGtnXuXPn6NKlS6RVl8vLy6NFixZZ1r2ioiL27NmTrcS8adOmOHjwYHZdGkE4evTomD17dkyaNCnXlh0CBAgQIECAAAECBAgQIECAAAECNQlIBNYkVMfz+/bty0bwVSUBBwwYEJMnT44hQ4ZkCcCamj98+HCsWLEim048b968SN9vvfXW6NGjRzZVuKbrnSdAgAABAgQIECBAgAABAgQIECCQBJpiqF+BhQsXxrJly7JOxo0bF8uXL4/0mUYB1qaUlZXFoEGDYu7cubFo0aJI31OZNm1aHDnyl3cM1KYddQgQIECAAAECBAgQIECAAAECBBq3gERgPT//pUuXZj307t07G9WXluE+3ZIWC7n77ruzy9MIww0bNpxuU64jQIAAAQIECBAgQIAAAQIECBBoZAKnn5VqZFCne7tLlizJLr322mtzo/lOt6103dixY3OXr127NrdvhwABAgQIECBAgAABAgQIECBAgMCpBCQCT6VTgHNbtmzJWrn44osL0FpE+/btcwnFAwcOFKRNjRAgQIAAAQIECBAgQIAAAQIECJS+gERgPT/j7t27Zz1UvSewrt2lqcZpwZBU+vXrV9fmXE+AAAECBAgQIECAAAECBAgQINBIBCQC6/lB9+/fP+vhwQcfjCeeeKJOve3atSumTJmStdGuXbvo1q1bndpzMQECBAgQIECAAAECBAgQIECAQOMRkAis52c9ffr0bCpvRUVFjBkzJlv999ChQ3n3umrVqhg2bFikz1QmTJiQdxsuIECAAAECBAgQIECAAAECBAgQaLwCzRvvrZ+ZO09Tg2fNmhVTp06N3bt3Zwm8tD948ODo27dvNqqvY8eOUV5eHi1btozKyspIScM9e/bE5s2bY/369fHkk0/GmjVrcgGnhOAdd9yR+26HAAECBAgQIECAAAECBAgQIECAQE0CEoE1CRXg/G233ZYt8jFx4sRIC3zs3bs3Hn300WzLt/nhw4fHggULomlTgznztVOfAAECBAgQIECAAAECBAgQINCYBWSTztDTHz9+fGzatClmzJgRnTp1yqvXFi1aZNOKH3nkkVi8eHGk9wMqBAgQIECAAAECBAgQIECAAAECBPIRMCIwH6061u3QoUPMnDkz2zZu3BjLly+PdevWZdOA07ThNFKwrKwsWrVqFa1bt440rbhnz57Rp0+f7Fgdu3c5AQIECBAgQIAAAQIECBAgQIBAIxaQCDxLD79r166RNoUAAQIECBAgQIAAAQIECBAgQIDAmRAwNfhMKOuDAAECBAgQIECAAAECBAgQIECAwFkWMCLwLD+ArVu3xo4dO2L//v3ZllYObtOmTTY1uH379tlKwmc5RN0TIECAAAECBAgQIECAAAECBAiUgIBE4Bl+iOk9gPPnz89W/l2zZk32XsDqQmjevHn06tUrBg4cGKNGjYoRI0ZEkyZNqqvuOAECBAgQIECAAAECBAgQIECAAIFqBUwNrpamsCe2bdsWEydOjIsuuihuvvnmWLZs2SmTgKn3ysrKWLlyZcyZMydLBPbu3Tsee+yxwgamNQIECBAgQIAAAQIECBAgQIAAgUYhYETgGXjMO3fujKFDh8bq1atzvaWRfZ07d44uXbpEWk24vLw8WrRokSX/KioqspWEN2/eHJs2bYqDBw9m16URhKNHj47Zs2fHpEmTcm3ZIUCAAAECBAgQIECAAAECBAgQIFCTgERgTUJ1PL9v374YOXJkLgk4YMCAmDx5cgwZMiRLANbU/OHDh2PFihXZdOJ58+ZF+n7rrbdGjx49sqnCNV3vPAECBAgQIECAAAECBAgQIECAAIEkYGpwPf8+WLhwYTYNOHUzbty4WL58efaZRgHWppSVlcWgQYNi7ty5sWjRokjfU5k2bVocOXKkNk2oQ4AAAQIECBAgQIAAAQIECBAgQEAisL5/DyxdujTrIr3fLy0S0rTp6ede02Ihd999d9Zemma8YcOG+g5f+wQIECBAgAABAgQIECBAgAABAiUicPpZqRIBqO/bWLJkSdbFtddemxvNV5c+x44dm7t87dq1uX07BAgQIECAAAECBAgQIECAAAECBE4lIBF4Kp0CnNuyZUvWysUXX1yA1iLat2+fSygeOHCgIG1qhAABAgQIECBAgAABAgQIECBAoPQFJALr+Rl3794962HZsmUF6SlNNU4LhqTSr1+/grSpEQIECBAgQIAAAQIECBAgQIAAgdIXkAis52fcv3//rIcHH3wwnnjiiTr1tmvXrpgyZUrWRrt27aJbt251as/FBAgQIECAAAECBAgQIECAAAECjUdAIrCen/X06dOzqbwVFRUxZsyYbPXfQ4cO5d3rqlWrYtiwYZE+U5kwYULebbiAAAECBAgQIECAAAECBAgQIECg8Qo0b7y3fmbuPE0NnjVrVkydOjV2796dJfDS/uDBg6Nv377ZqL6OHTtGeXl5tGzZMiorKyMlDffs2RObN2+O9evXx5NPPhlr1qzJBZwSgnfccUfuux0CBAgQIECAAAECBAgQIECAAAECNQlIBNYkVIDzt912W7bIx8SJEyMt8LF379549NFHsy3f5ocPHx4LFiyIpk0N5szXTn0CBAgQIECAAAECBAgQIECAQGMWkE06Q09//PjxsWnTppgxY0Z06tQpr15btGiRTSt+5JFHYvHixZHeD6gQIECAAAECBAgQIECAAAECBAgQyEfAiMB8tOpYt0OHDjFz5sxs27hxYyxfvjzWrVuXTQNO04bTSMGysrJo1apVtG7dOtK04p49e0afPn2yY3Xs3uUECBAgQIAAAQIECBAgQIAAAQKNWEAi8Cw9/K5du0baFAIECBAgQIAAAQIECBAgQIAAAQJnQsDU4DOhrA8CBAgQIECAAAECBAgQIECAAAECZ1nAiMCz/AC2bt0aO3bsiP3792dbWjm4TZs22dTg9u3bZysJn+UQdU+AAAECBAgQIECAAAECBAgQIFACAhKBZ/ghpvcAzp8/P1v5d82aNdl7AasLoXnz5tGrV68YOHBgjBo1KkaMGBFNmjSprrrjBAgQIECAAAECBAgQIECAAAECBKoVMDW4WprCnti2bVtMnDgxLrroorj55ptj2bJlp0wCpt4rKytj5cqVMWfOnCwR2Lt373jssccKG5jWCBAgQIAAAQIECBAgQIAAAQIEGoWAEYFn4DHv3Lkzhg4dGqtXr871lkb2de7cObp06RJpNeHy8vJo0aJFlvyrqKjIVhLevHlzbNq0KQ4ePJhdl0YQjh49OmbPnh2TJk3KtWWHAAECBAgQIECAAAECBAgQIECAQE0CEoE1CdXx/L59+2LkyJG5JOCAAQNi8uTJMWTIkCwBWFPzhw8fjhUrVmTTiefNmxfp+6233ho9evTIpgrXdL3zBAgQIECAAAECBAgQIECAAAECBJKAqcH1/Ptg4cKF2TTg1M24ceNi+fLl2WcaBVibUlZWFoMGDYq5c+fGokWLIn1PZdq0aXHkyJHaNKEOAQIECBAgQIAAAQIECBAgQIAAgTAisJ5/EyxdujTrIb3fLy0S0rTp6ede02Ihd999d9xyyy3ZCMMNGzZE9+7d6/kOIr73ve/Ft771rTh69Gid+0rTnVNJ70xUCBAgQIAAAQIECBAgQIAAAQIEzpyARGA9Wy9ZsiTr4dprr82N5qtLl2PHjs0SgamNtWvXnpFE4OOPPx7PPPNMXcI+4dpXXnnlhGMOECBAgAABAgQIECBAgAABAgQI1J+ARGD92WYtb9myJfu8+OKLC9JT+/bts4RielfggQMHCtJmTY3cf//9MXXq1IJMRU4LnfzgBz+IPn361NSt8wQIECBAgAABAgQIECBAgAABAgUUkAgsIObJmkpTd1etWpW9J/Cmm246WZW8jqWpxikJmEq/fv3yuvZ0K6fVjPv27Xu6l7/pugsuuOBN330hQIAAAQIECBAgQIAAAQIECBA4MwKn/8K6MxNfg++lf//+2T08+OCD8cQTT9Tpfnbt2hVTpkzJ2mjXrl1069atTu25mAABAgQIECBAgAABAgQIECBAoPEISATW87OePn16NpW3oqIixowZk63+e+jQobx7TaMKhw0blo0uTBdPmDAh7zZcQIAAAQIECBAgQIAAAQIECBAg0HgFTA2u52efpgbPmjUre8fe7t27swReet/e4MGDs+m2aVRfx44do7y8PFq2bBmVlZWRkoZ79uyJtMLu+vXr48knn4w1a9bkIk0JwTvuuCP33Q4BAgQIECBAgAABAgQIECBAgACBmgQkAmsSKsD52267LdIiHxMnTswW+Ni7d288+uij2ZZv88OHD48FCxZE06YGc+Zrpz4BAgQIECBAgAABAgQIECBAoDELyCadoac/fvz42LRpU8yYMSM6deqUV69psY40rfiRRx6JxYsXR3o/oEKAAAECBAgQIECAAAECBAgQIEAgHwEjAvPRqmPdDh06xMyZM7Nt48aNsXz58li3bl02DThNG04jBcvKyqJVq1bRunXrSNOKe/bsGX369MmO1bF7lxMgQIAAAQIECBAgQIAAAQIECDRiAYnAs/Twu3btGmlTCBAgQIAAAQIECBAgQIAAAQIECJwJAYnAM6F8ij62bt0aO3bsiP3792dbWjCkTZs22YjA9F7B9F0hQIAAAQIECBAgQIAAAQIECBAgUFcBicC6CuZ5fZr+O3/+/GzBj7QScPpeXWnevHn06tUrBg4cGKNGjYoRI0ZEkyZNqqvuOAECBAgQIECAAAECBAgQIECAAIFqBSwWUi1NYU9s27YtWzX4oosuiptvvjmWLVt2yiRg6r2ysjJWrlwZc+bMyRKBvXv3jscee6ywgWmNAAECBAgQIECAAAECBAgQIECgUQgYEXgGHvPOnTtj6NChsXr16lxvaWRf586do0uXLpEWESkvL4+0OnBK/lVUVGQLiGzevDlbafjgwYPZdWkE4ejRo2P27NkxadKkXFt2CBAgQIAAAQIECBAgQIAAAQIECNQkIBFYk1Adz+/bty9GjhyZSwIOGDAgJk+eHEOGDMkSgDU1f/jw4VixYkU2nXjevHmRvt96663Ro0ePbKpwTdc7T4AAAQIECBAgQIAAAQIECBAgQCAJmBpcz78PFi5cmE0DTt2MGzculi9fnn2mUYC1KWVlZTFo0KCYO3duLFq0KNL3VKZNmxZHjhypTRPqECBAgAABAgQIECBAgAABAgQIEJAIrO/fA0uXLs26SO/3S4uENG16+rnXtFjI3XffnbWXphlv2LChvsPXPgECBAgQIECAAAECBAgQIECAQIkInH5WqkQA6vs2lixZknVx7bXX5kbz1aXPsWPH5i5fu3Ztbt8OAQIECBAgQIAAAQIECBAgQIAAgVMJSASeSqcA57Zs2ZK1cvHFFxegtYj27dvnEooHDhwoSJsaIUCAAAECBAgQIECAAAECBAgQKH0BicB6fsbdu3fPeli2bFlBekpTjdOCIan069evIG1qhAABAgQIECBAgAABAgQIECBAoPQFJALr+Rn3798/6+HBBx+MJ554ok697dq1K6ZMmZK10a5du+jWrVud2nMxAQIECBAgQIAAAQIECBAgQIBA4xGQCKznZz19+vRsKm9FRUWMGTMmW/330KFDefe6atWqGDZsWKTPVCZMmJB3Gy4gQIAAAQIECBAgQIAAAQIECBBovALNG++tn5k7T1ODZ82aFVOnTo3du3dnCby0P3jw4Ojbt282qq9jx45RXl4eLVu2jMrKykhJwz179sTmzZtj/fr18eSTT8aaNWtyAaeE4B133JH7bocAAQIECBAgQIAAAQIECBAgQIBATQISgTUJFeD8bbfdli3yMXHixEgLfOzduzceffTRbMu3+eHDh8eCBQuiaVODOfO1U58AAQIECBAgQIAAAQIECBAg0JgFZJPO0NMfP358bNq0KWbMmBGdOnXKq9cWLVpk04ofeeSRWLx4caT3AyoECBAgQIAAAQIECBAgQIAAAQIE8hEwIjAfrTrW7dChQ8ycOTPbNm7cGMuXL49169Zl04DTtOE0UrCsrCxatWoVrVu3jjStuGfPntGnT5/sWB27dzkBAgQIECBAgAABAgQIECBAgEAjFpAIPEsPv2vXrpE2hQABAgQIECBAgAABAgQIECBAgMCZEDA1+Ewo64MAAQIECBAgQIAAAQIECBAgQIDAWRYwIvAsP4Dju9+3b18899xzsXr16njttdfi0ksvjcsvvzx69OgRzZo1O7667wQIECBAgAABAgQIECBAgAABAgRqJSARWCum06/0+OOPx7PPPps1cOONN8Y555xz0sYOHToUs2bNyrbDhw+fUOeyyy6Lu+66K1s05ISTDhAgQIAAAQIECBAgQIAAAQIECBCoQUAisAagup7+0Y9+FHPmzMmaGTdu3EkTgWk14VGjRsWaNWuq7e53v/tdfOADH4gRI0bEww8/HM2be3TVYjlBgAABAgQIECBAgAABAgQIECBwgoBs0gkkZ/bA0aNH4/rrr39TErBfv37ZSsFdunSJlCR8/vnn49/+7d+ywH72s5/F5MmT42tf+9qZDVRvBAgQIECAAAECBAgQIECAAAECDVpAIvAsP7777rsvfv3rX2dRdOrUKb75zW/GBz/4wROi+tWvfhUTJ06MF154Ib7+9a9H//794+Mf//gJ9RwgQIAAAQIECBAgQIAAAQIECBAgcDIBqwafTOUMHnvggQey3po0aRILFy48aRIwVbjmmmuyhGFKFqZyzz33ZJ9+IUCAAAECBAgQIECAAAECBAgQIFAbAYnA2ijVU5033ngjWx04NX/dddfF+973vlP2dMEFF8S9996b1UnThQ8cOHDK+k4SIECAAAECBAgQIECAAAECBAgQqBKQCKySOAufGzdujIqKiqznq666qlYRpJGBqVRWVsaqVatqdY1KBAgQIECAAAECBAgQIECAAAECBCQCz+LvgY4dO0aaEpxKGu1Xm9KhQ4do2bJlVjUtJKIQIECAAAECBAgQIECAAAECBAgQqI2ARGBtlOqpTqtWrSKtDJzKsmXLatXLsaMIu3fvXqtrVCJAgAABAgQIECBAgAABAgQIECAgEXgGfw/89re/zU0Frur2k5/8ZLb7+OOPx9GjR6sOV/s5e/bs3LnLLrsst2+HAAECBAgQIECAAAECBAgQIECAwKkEmp/qpHOFFRg+fHg0b948rrjiiujfv3+8613vine/+93RunXrePHFF+O2226LYxN9x/aekoT/9E//FN/61reyw+ldgek6hQABAgQIECBAgAABAgQIECBAgEBtBCQCa6NUhzpV7wCsaiIt8pFGBqbt/vvvrzqcfd5zzz3Rp0+f+NjHPpY7/tprr8V9990XDz/8cKxcuTI73qxZs9zqwbmKdggQIECAAAECBAgQIECAAAECBAicQkAi8BQ4hTiVknif+cxnsiReWuW3atu+fftJmz98+PCbjm/ZsiU+//nP546lxOIXv/jF6N27d+6YHQIECBAgQIAAAQIECBAgQIAAAQI1CUgE1iRUx/NNmzaNyy+/PNv+/u//Ptfa1q1bc0nBlBxMo/1eeuml6NatW65O2jl2NeH27dvH/PnzY8SIEW+q4wsBAgQIECBAgAABAgQIECBAgACBmgQkAmsSqqfzF154YaTt2KTe3r174y1vecubeuzQoUN89rOfjaFDh8Z73/veaNmy5ZvO+0KAAAECBAgQIECAAAECBAgQIECgNgISgbVROkN1zjvvvBN6SouL3HXXXSccd4AAAQIECBAgQIAAAQIECBAgQIBAPgJN86msLgECBAgQIECAAAECBAgQIECAAAECDVNAIrBhPjdREyBAgAABAgQIECBAgAABAgQIEMhLQCIwLy6VCRAgQIAAAQIECBAgQIAAAQIECDRMAYnAhvncRE2AAAECBAgQIECAAAECBAgQIEAgLwGJwLy4VCZAgAABAgQIECBAgAABAgQIECDQMAUkAhvmcxM1AQIECBAgQIAAAQIECBAgQIAAgbwEJALz4lKZAAECBAgQIECAAAECBAgQIECAQMMUkAhsmM9N1AQIECBAgAABAgQIECBAgAABAgTyEpAIzItLZQIECBAgQIAAAQIECBAgQIAAAQINU0AisGE+N1ETIECAAAECBAgQIECAAAECBAgQyEtAIjAvLpUJECBAgAABAgQIECBAgAABAgQINEwBicCG+dxETYAAAQIECBAgQIAAAQIECBAgQCAvAYnAvLhUJkCAAAECBAgQIECAAAECBAgQINAwBSQCG+ZzEzUBAgQIECBAgAABAgQIECBAgACBvAQkAvPiUpkAAQIECBAgQIAAAQIECBAgQIBAwxSQCGyYz03UBAgQIECAAAECBAgQIECAAAECBPISkAjMi0tlAgQIECBAgAABAgQIECBAgAABAg1TQCKwYT43URMgQIAAAQIECBAgQIAAAQIECBDIS0AiMC8ulQkQIECAAAECBAgQIECAAAECBAg0TAGJwIb53ERNgAABAgQIECBAgAABAgQIECBAIC8BicC8uFQmQIAAAQIECBAgQIAAAQIECBAg0DAFJAIb5nMTNQECBAgQIECAAAECBAgQIECAAIG8BCQC8+JSmQABAgQIECBAgAABAgQIECBAgEDDFJAIbJjPTdQECBAgQIAAAQIECBAgQIAAAQIE8hKQCMyLS2UCBAgQIECAAAECBAgQIECAAAECDVNAIrBhPjdREyBAgAABAgQIECBAgAABAgQIEMhLQCIwLy6VCRAgQIAAAQIECBAgQIAAAQIECDRMAYnAhvncRE2AAAECBAgQIECAAAECBAgQIEAgLwGJwLy4VCZAgAABAgQIECBAgAABAgQIECDQMAUkAhvmcxM1AQIECBAgQIAAAQIECBAgQIAAgbwEJALz4lKZAAECBAgQIECAAAECBAgQIECAQMMUkAhsmM9N1AQIECBAgAABAgQIECBAgAABAgTyEpAIzItLZQIECBAgQIAAAQIECBAgQIAAAQINU0AisGE+N1ETIECAAAECBAgQIECAAAECBAgQyEtAIjAvLpUJECBAgAABAgQIECBAgAABAgQINEwBicCG+dxETYAAAQIECBAgQIAAAQIECBAgQCAvAYnAvLhUJkCAAAECBAgQIECAAAECBAgQINAwBSQCG+ZzEzUBAgQIECBAgAABAgQIECBAgACBvAQkAvPiUpkAAQIECBAgQIAAAQIECBAgQIBAwxSQCGyYz03UBAgQIECAAAECBAgQIECAAAECBPISkAjMi0tlAgQIECBAgAABAgQIECBAgAABAg1TQCKwYT43URMgQIAAAQIECBAgQIAAAQIECBDIS0AiMC8ulQkQIECAAAECBAgQIECAAAECBAg0TAGJwIb53ERNgAABAgQIECBAgAABAgQIECBAIC8BicC8uFQmQIAAAQIECBAgQIAAAQIECBAg0DAFJAIb5nMTNQECBAgQIECAAAECBAgQIECAAIG8BCQC8+JSmQABAgQIECBAgAABAgQIECBAgEDDFJAIbJjPTdQECBAgQIAAAQIECBAgQIAAAQIE8hKQCMyLS2UCBAgQIECAAAECBAgQIECAAAECDVNAIrBhPjdREyBAgAABAgQIECBAgAABAgQIEMhLQCIwLy6VCRAgQIAAAQIECBAgQIAAAQIECDRMAYnAhvncRE2AAAECBAgQIECAAAECBAgQIEAgLwGJwLy4VCZAgAABAgQIECBAgAABAgQIECDQMAUkAhvmcxM1AQIECBAgQIAAAQIECBAgQIAAgbwEJALz4lKZAAECBAgQIECAAAECBAgQIECAQMMUkAhsmM9N1AQIECBAgAABAgQIECBAgAABAgTyEpAIzItLZQIECBAgQIAAAQIECBAgQIAAAQINU0AisGE+N1ETIECAAAECBAgQIECAAAECBAgQyEtAIjAvLpUJECBAgAABAgQIECBAgAABAgQINEwBicCG+dxETYAAAQIECBAgQIAAAQIECBAgQCAvAYnAvLhUJkCAAAECBAgQIECAAAECBAgQINAwBSQCG+ZzEzUBAgQIECBAgAABAgQIECBAgACBvAQkAvPiUpkAAQIECBAgQIAAAQIECBAgQIBAwxSQCGyYz03UBAgQIECAAAECBAgQIECAAAECBPISkAjMi0tlAgQIECBAgAABAgQIECBAgAABAg1TQCKwYT43URMgQIAAAQIECBAgQIAAAQIECBDIS0AiMC8ulQkQIECAAAECBAgQIECAAAECBAg0TAGJwIb53ERNgAABAgQIECBAgAABAgQIECBAIC8BicC8uFQmQIAAAQIECBAgQIAAAQIECBAg0DAFJAIb5nMTNQECBAgQIECAAAECBAgQIECAAIG8BCQC8+JSmQABAgQIECBAgAABAgQIECBAgEDDFJAIbJjPTdQECBAgQIAAAQIECBAgQIAAAQIE8hKQCMyLS2UCBAgQIECAAAECBAgQIECAAAECDVNAIrBhPjdREyBAgAABAgQIECBAgAABAgQIEMhLQCIwLy6VCRAgQIAAAQIECBAgQIAAAQIECDRMAYnAhvncRE2AAAECBAgQIECAAAECBAgQIEAgLwGJwLy4VCZAgAABAgQIECBAgAABAgQIECDQMAWaN8ywSyfqrVu3xo4dO2L//v3Z1rJly2jTpk20bt062rdvH+m7QoAAAQIECBAgQIAAAQIECBAgQKCuAhKBdRXM8/q9e/fG/PnzY8GCBbFmzZpI36srzZs3j169esXAgQNj1KhRMWLEiGjSpEl11R0nQIAAAQIECBAgQIAAAQIECBAgUK2AqcHV0hT2xLZt22LixIlx0UUXxc033xzLli07ZRIw9V5ZWRkrV66MOXPmZInA3r17x2OPPVbYwLRGgAABAgQIECBAgAABAgQIECDQKASMCDwDj3nnzp0xdOjQWL16da63NLKvc+fO0aVLl+jQoUOUl5dHixYtsuRfRUVF7NmzJzZv3hybNm2KgwcPZtelEYSjR4+O2bNnx6RJk3Jt2SFAgAABAgQIECBAgAABAgQIECBQk4BEYE1CdTy/b9++GDlyZC4JOGDAgJg8eXIMGTIkSwDW1Pzhw4djxYoV2XTiefPmRfp+6623Ro8ePbKpwjVd7zwBAgQIECBAgAABAgQIECBAgACBJGBqcD3/Pli4cGE2DTh1M27cuFi+fHn2mUYB1qaUlZXFoEGDYu7cubFo0aJI31OZNm1aHDlypDZNqEOAAAECBAgQIECAAAECBAgQIEAgjAis598ES5cuzXpI7/dLi4Q0bXr6ude0WMjdd98dt9xySzbCcMOGDdG9e/d6voOIb33rW3HfffcVJPGY3pWYSlotuVjLD2/+UDRtVrx/NI6+8UZGt3zonGhS1qxYGWP/hh1FG9vJAvvpjPHRrKzFyU4VxbHKQxVZHM98+PvRtOVf/odAUQR2XBAHNu087khxf/3ZrFujrGV50QZZsXdXFtuzN/4omp37luKNc8tf4izaAI8L7PGv/f/jqe9+5bijxfN17/Y/ZsG88I+PxrqZ/1I8gR0XycE/7jnuSHF/Xfr9e+KZH3+vaIPc/cc/ZLGt/9IvY9PcZUUb56HtrxdtbCcLLD3z53/x05OdKopje7a/nMWxac7S+ONDzxZFTCcL4vCO/Sc7XLTHnvv5j2PjiieKNr7XX30li23rP6+M1379UtHGWbn7QNHGdmxgR48ezb6+tPQX8d2P/v+OPVVU+/t3vprFs/3nv4tfvePOoort2GDeeP0vryY79pj90hIo3mxHiTgvWbIku5Nrr702N5qvLrc2duzYLBGY2li7du0ZSQSmUYzPP/98XcI+4dpmzYovgdW1a9csQbnr5Y0nxFuMB/a/9FoxhvWmmM4595w4//zz33Ss2L6k5/7ss8/G7j9uLrbQThrPgY3Fn2gre0tZXHjhhSeNv1gOpueeyt7/+A+w7EsR/1KxufgTbU2aNsnee1vEjNGtW7csvKr/ACvmWFNsB7fuybZij7Pqz1OxxlkV374df4q0FXtJibaGkGyrci1Wz6r4DuzeEWkr9nL4tf2RtmIvVa7FGmdVfAf37o60FXup3FURaSv2UuVarHFecsklWWiH9r8eO/6wvljDzMX1xt6Dse/F7bnvxbpT9e+mYo1PXKcv0OTP2fO/pM9Pvw1XnkKgdevW2erAaeXfm2666RQ1a3cqLSSS2kzvCnzooYfiQx/6UO0urEOt1Nfvf//7KNRvlbQQylVXXVWHiOrn0gMHDmSLs9RP64VrNS0ek2Jt27Zt4Rqtp5bSFPj27dvXU+uFafbQoUPZ7+/CtFZ/raQ/h6+//nq89a1vrb9OCtRyu3bt4oILLihQa/XTTFqV/aWXXirYz7X6iTLijT+PAN61a1fR/zlK99+mTZtsEaz6sihEu+mVGuvXry/ICPdCxFNdG+nv21dffbVW7xKuro0zdfzcc8+Niy+++Ex1d9r9pOee/twXe9m+fXvR//xMhi1btoxiTwykONO/X9Pf88Ve0oyZjh07FnuY2aCGMzEbqa4QabHF9G/lYi8N5c97GsDx9re/PdJil8VcXn755ey/u4s5xhTbn/70p+zfdXWZKXgm7jHFl/68F+MAnjNx/6Xeh0RgPT/hfv36xapVq+LjH/94fP/7369zb48//ni20EhqKP3jRpa+zqQaIECAAAECBAgQIECAAAECBAg0CoHTf2Fdo+Cp+032798/a+TBBx+MJ554ok4NplEhU6ZMydpII24kAevE6WICBAgQIECAAAECBAgQIECAQKMSkAis58c9ffr0bBh9mtI7ZsyYbPXf05mikEYVDhs2LBtdmEKeMGFCPUeueQIECBAgQIAAAQIECBAgQIAAgVISMDX4DDzNtNLv1KlTcz2dd955MXjw4Ojbt282qi+9E6S8vDx730p6f01KGqb36G3evDl7l9GTTz4Za9asyV2fEoKLFy+u0wrEucbsECBAgAABAgQIECBAgAABAgQINAoBicAz9JjnzZsXEydOrPOLa4cPHx4LFiyINDVYIUCAAAECBAgQIECAAAECBAgQIFBbAYnA2koVoF5aIejee++N+++/P1555ZVat9iiRYtICcAbbrghRo0aVevrVCRAgAABAgQIECBAgAABAgQIECBQJSARWCVxhj83btwYy5cvj3Xr1mXTgHfv3p0td15WVhatWrWK1q1bZ8t19+zZM/r06ZMdO8Mh6o4AAQIECBAgQIAAAQIECBAgQKCEBCQCS+hhuhUCBAgQIECAAAECBAgQIECAAAEC1QlYNbg6GccJECBAgAABAgQIECBAgAABAgQIlJCARGAJPUy3QoAAAQIECBAgQIAAAQIECBAgQKA6AYnA6mQcJ0CAAAECBAgQIECAAAECBAgQIFBCAhKBJfQw3QoBAgQIECBAgAABAgQIECBAgACB6gQkAquTcZwAAQIECBAgQIAAAQIECBAgQIBACQlIBJbQw3QrBAgQIECAAAECBAgQIECAAAECBKoTkAisTsZxAgQIECBAgAABAgQIECBAgAABAiUkIBFYQg/TrRAgQIAAAQIECBAgQIAAAQIECBCoTkAisDoZxwkQIECAAAECBAgQIECAAAECBAiUkIBEYAk9TLdCgAABAgQIECBAgAABAgQIECBAoDoBicDqZBwnQIAAAQIECBAgQIAAAQIECBAgUEICEoEl9DDdCgECBAgQIECAAAECBAgQIECAAIHqBCQCq5NxnAABAgQIECBAgAABAgQIECBAgEAJCUgEltDDdCsECBAgQIAAAQIECBAgQIAAAQIEqhOQCKxOxnECBAgQIECAAAECBAgQIECAAAECJSQgEVhCD9OtECBAgAABAgQIECBAgAABAgQIEKhOQCKwOhnHCRAgQIAAAQIECBAgQIAAAQIECJSQgERgCT1Mt0KAAAECBAgQIECAAAECBAgQIECgOgGJwOpkHCdAgAABAgQIECBAgAABAgQIECBQQgISgSX0MN0KAQIECBAgQIAAAQIECBAgQIAAgeoEJAKrk3GcAAECBAgQIECAAAECBAgQIECAQAkJSASW0MN0KwQIECBAgAABAgQIECBAgAABAgSqE5AIrE7GcQIECBAgQIAAAQIECBAgQIAAAQIlJCARWEIP060QIECAAAECBAgQIECAAAECBAgQqE5AIrA6GccJECBAgAABAgQIECBAgAABAgQIlJCARGAJPUy3QoAAAQIECBAgQIAAAQIECBAgQKA6AYnA6mQcJ0CAAAECBAgQIECAAAECBAgQIFBCAhKBJfQw3QoBAgQIECBAgAABAgQIECBAgACB6gQkAquTcZwAAQIECBAgQIAAAQIECBAgQIBACQlIBJbQw3QrBAgQIECAAAECBAgQIECAAAECBKoTkAisTsZxAgQIECBAgAABAgQIECBAgAABAiUkIBFYQg/TrRAgQIAAAQIECBAgQIAAAQIECBCoTkAisHImqMIAADW/SURBVDoZxwkQIECAAAECBAgQIECAAAECBAiUkIBEYAk9TLdCgAABAgQIECBAgAABAgQIECBAoDoBicDqZBwnQIAAAQIECBAgQIAAAQIECBAgUEICEoEl9DDdCgECBAgQIECAAAECBAgQIECAAIHqBCQCq5NxnAABAgQIECBAgAABAgQIECBAgEAJCTQvoXtxKwTOqMBrr70W99xzTzzzzDOxbt26+OMf/xjdunWLyy67LN73vvfFxIkT4y1veUuNMb300kvxpS99KWsn7b/tbW+L97znPTFo0KC47rrr4txzz62xjZNVWLp0aRbHW9/61nj11VdPVuWEY9u3b4+77rorVqxYES+++GIcPHgwunTpEsOHD49PfvKT2b2dcFEjO1CKz726R/jNb34zbr/99uz0U089FW9/+9urq1ryx0vtuR85ciRuvvnmSJ+1Keln0fvf//7aVC2pOoV67m+88UZ8//vfjx/+8IfZ3xd79uyJq666Kvs5P2LEiHjXu951Wm6n83M+dZR+tqd4/u3f/i1+85vfxPr16+OSSy6Jyy+/PP7hH/4hhgwZclrxlMpFpfLcH3vssfjpT396Wo/lpptuigEDBpzWtQ31omJ/7sn1v/yX/xL/7//9v/j617+e/QyvjXX6N919990XL7zwQvzud7+LNm3aRM+ePWPgwIExderUOO+882rTTEnXSX9WfvCDH2Q/n9O/6dO/39O/59/xjnfEpz71qejbt2+N919fP+dTx6fz3B9++OFI26pVq7Ln3qlTp+x+brjhhhgzZkw0adKkxnsq9QrF9NwL9fOnvv67stR/LzTK+zuqECCQt8BXv/rVo23btj365x8a1W5//gfE0V/96lenbPsrX/nK0bKysmrbuPrqq4/u2LHjlG2c7GS6pkePHlm77du3P1mVE459+9vfPtq6detqY/lzQvLon//xecJ1jelAKT736p7fc889d7Rly5a53w9//g+I6qqW/PFSfO7peZ7q59fx5/7pn/6p5J/z8TdYqOe+ZcuWo1deeWW13s2bNz/6wAMPHN99jd9P5+d8avQPf/jD0T8neKqNJz37D33oQ0cPHDhQYwylWKGUnvsXv/jFUz7n4/+cH/v9n//5n0vx8VZ7T8X+3FPg3/jGN3LP88+JwGrv5f9r707A7pjuOI4foiVBbbE01sfWkoYqJU/tSxBLRUsJKkhsT+3xhFpqqa3a0HoEtVRtpQSxRGrfHluUCEGtIfZqq7YIwfT8TnqOuft933cmzp35nudJ7tyZufPOfP73zr3zn7P4BTbhn+y3337JnHPOGV6XjrGm+/Xrl9hkkX9J6R5t0iTZbLPNGvrIqFevXslBBx2UfPTRRw198jrP6w92Ne7vvfdesuuuuzY9JpvYdN8FDQ+o4Atii3tW55+8risL/nYo7eGZ0h45B45ANwUuv/zyii9Xe5cuOeWUUxJbuyI55phjEnuXNSzv3bt3ooRKvfKnP/0prKeEy7Bhw5LzzjsvOfroo5PVV189LBswYEDy9ttv19tE3Xnvv/9+xUVeO4nAm2++ObF3BsPf3GmnnRJbGyy56KKL3I9In6zU4xVXXFH37xZ9ZhHj3ihmunjQj8T0BUNZE4FFjbut+VAR33Ss602XLRGYVdx1Pl5ttdWCtT5Xxx57rDvX77LLLom+I+St86+tsdPoI1kzvzvneW3k4YcfTvr27Rv2x9ZiTw477LDk4osvdhe6ffr0CctsLfCav1v0GUWLe08SgbfddlvRwx2OL/a4a0f12yud0GsnEThy5MjweZ577rkTWwvcfdaVdEgnv2ztt2TSpEnBoywTutmh39j+O2+xxRZLDj300OSCCy5IZLT77rsnulHjl+t5vZLXeV5/q6tx/+yzzyqOSbHVfus3/emnn55svvnm4XiWXHLJ5Pnnn693SIWeF1vcszr/5HVdWeg3Q8kPjkRgyd8AHH7XBF5++eXENqFwX6JKitkmNzUb0JfwgQceGL5odeGneelim+CGC0DbRCO555570ovd+jvvvHPYhm2qVbG80RPbTCyxzRjC6/TjpVUi0FZFT7QPWlc/Mm+66aaazT/++OOJv0DUj6JXX321Zp0izyhi3JvFyzYVqngP6b1RxkRgkeM+atSoEGPV9LVdGzT99/HHHzd7yxRqWVZxF0r6Qtw2r06UZE+X+++/P5x/dW5944030ovrTnfnPK8NzZgxI1lppZVC3HVRWF30OVcNIX/he+edd1avUtjnRYy7ajDpRmI7/4YOHRriXqYkcOxxnz59eqLvZNVK859LPbZKBOo84W/w2i5ikmeeeabms2u7pQnb1E1s27S1Zp0iz7Bd+ITj32KLLRL9Hq4utlltsvjii4f1rrnmmupVcjnPdzfuJ598cthX27VPYpuF1+yvEp3+/bTRRhvVLC/6jJjintX5J6/ryqK/F8p+fCQCy/4O4Pi7JKCaf/6H2FFHHdXwtZ9//nlFrTzVwkgXvdZvR7UA6xX9IPPNt+abb77E9ilVbzU3Tz/2Dz744Iq7xX77rRKB+kHg19U2GpVf//rXYT3bb1yj1Qo5v4hxbxQoNWf3tQ580lvvjzImAoscd18rQMmnsjYBbfQZyCruuqjUuVufH12QVScB/d/XzRd/Dj7uuOP87JrHnpzntbHRo0eHv6MLoUZFzZT9/uimVllKUePeTvzUDNjHXF2SNHqvtrOtTlsn1rjL8d57761I3vsY6bFVIvBXv/pViKlqg9Urto/YxPYTGNYr0/f8zJkzE3V5I0slSpu1vFHTaW9v+8yuoMz6PK+Ndzfub775ZqhkoP21fTtX7Gv6ie33MBzT2LFj04sKPR1b3LM6/2R9XVnoNwEHFwRIBAYKJhBoLaA7hv7HwCuvvNL0BaeddlpYt/pHmO2w1y1ToqXZD27bsXzYRqNmY0oyqnmX3y8lcfSF4P9Gq0SgftT419pO4xsek2ot+vVUW7FMpYhxrxc/9Suz9NJLuzjrmNVE3MfcDh5T7yWFnlfkuKsJlGKrbggolQJZxV03efznRz/2mxX1Kat1v/3tb9fUINfrenqe1zZ8E2XVALcDSGlW3aKEo75T9N4YPnx43XWKOLOocW8VK53bfcJa7412aqW22mYnLY8x7krQqSWIr9Gnc4MdyKeitUmrROA222wTzj/1agP6GP3yl78M66mJYlnKQw89FI57jz32aHrYujHvk4b67kyXLM/zPY17OmG54447pnezZvqdd94Jx6/femUpscU9q/OPv+bL4rqyLO8FjjNJ5rRfLhQEEGhTwJ5ozcCBA91IYhpNt1mxF3Rhse1EOEzbBKKxdx7d8w033LDpyMIaudH+EHTr2n5CwjbSE7Z/PzN16lQ3yzbpMnfccYexTQOMrenj5vnXp1+TntaIYpMnTzba/g9+8IP0ooppjXLpS/rY/LwiPxYx7vXipZHxXnvtNbPwwgsb29dIeO/VW7cM84oad1trwGiEcJU111yzDKHs0jFmEXf9QXvBEf6urYEZputN2P663GyNPm9r5das0tPzvO0Hyjz55JNuu9tuu62xN4hq/oafoZHqbXMlN9LkhRde6GcX/rGIcW8naPvss4+xyV+3qk1YG/2OKFOJMe4azd3eQFZlDRcKm5A3jz32mLHJ+RCaVr/twop2Iv37LT1f0xrp1hfbjY2fLPyjTAYNGmT69+/f8nvQ3mA3NgHoTN5991036roHyvI839O424Sv3y2z/vrrh+l6Ezoe/1m/6667wnut3rpFmhdb3LM4/2R9XVmkeHMszQVmZQqar8NSBBD4v4AdEKRtCyXXfLE1MfyksTU7wrTtPzBM15tYdNFFjZJuunCfMmVKvVXcPF3U2Q7fje0I2tiRfxuuV2+B7UjYaP/S+1hvvdtvvz3M3nrrrcN0GSaKGPfquCkRbJuHudnnnntu+IFYvV6Znhc17rbPoxDGtdZaK0wrGWBrCZjlllvO2P6DwvyyTWQRd5n5c70uIludX9MX+DrX10sc9uQ8rySCL5tssomf5DElUMS4pw6v7qTO+/fdd59bZpuIGjvCbN31ijwz1rjLXOcB28TXrLvuul0OwRprrGF0A0HFdj9gFN/qYruxMbaP2DC71W/SsGIBJmRb7zxb79DsYCBGyRYVW3vb2IFX3LT+y/o8r212N+7PPfecXu6K9rNV0Xe9ri+U3HzxxReN7UO21Us6fnlscc/i/OPfgwpOq89wu9eVHR9oDqAtAWoEtsXESgh0TeDDDz90Nez8q9Zee20/aV544YUwbZtfhelGE77mobapL+zqYjugdz9QbHPgLicBq7fV6LkdPdjYpslusR1lzGywwQaNVi31/E6Nux38xdg+w1zsdt11V6P3FKV9gU6LezoRuPLKK7saxDoX6SbCiiuuaGwzQaNzlm16VppaAu1H+6s1m8Vda+nCSkXnTDu4lJtu9J8/z2u5bapZs1pPz/NPPfVU2Ka/UFDNdCWC9t13X1cjZocddjB28ICw3+EFTFQIdFLcK3a86omOww5C4eYq8W+bOBolrSn1BWZn3BUHXdzfeuut3UoC6gj0nW6bCbqDsQMDGbX+SBfV/rP9Qoeawqodp+QhpVbg/PPPD9+F6d/zWjPL83xP456u6d2sFqg/Qt86Sc9tdxF+No//F5hdcW8HvNn5J+vrynb2h3WKIUCNwGLEkaOITMAOrOFq1mi3bP8PJp3wS38525HIWu657t748p///KemptaAAQP84swe9ePAdjLsfuBcffXVZtKkSW7bdvASM27cuJYXtZntSIdtqBPjrqYou+++u9Edb9s/YEj4dhj917q7nRb3dCLwJz/5SU2zMTu6rHn00Ufdv+uuu87ojvWyyy77tRrH+Mebxd2Oshya3HXnPF99vD09z6upry+6WNT53fYh5j73fr4dHd5ce+21LjH8+9//3owYMcIv4jEl0ElxT+12zeQZZ5xh1BRdRYlmnyCuWZEZTmB2xl3NfuvV4OtKKHTeUW0/dQWg345DhgxxzUXVBYxqf9t+n81LL73kNqm/9de//rUrmy/NuvqMqLsdFSXq1JTel6zP8z2N+yqrrOJ3zejmj27uNCp2kDCjm8C+/Pe///WTPFqB2Rn3dsCbnX+yvq5sZ39YpxgCJAKLEUeOIiIBXTT/9re/dXukGjZ2VN6KvVPCxZfevXv7yYaP6XWmT5/ecL0sF9x4442ulkh6m0oS6Y5y2foHTBs0m+7UuOu9qqZh+gGqY1hwwQWbHSbLqgQ6Me4+sa9D0Q9I2wm9UXNRO1qo6ztw4sSJRjcA1D+VLhaVMFLTUnUjQJkl0GlxV20CX9Sn1Z577ml0IagbTb5vWMVYtUJ0cbv33nu7i0RdfFC+Eui0uH+155VT+i3ha/nr3H/00UdXrsCzCoFOjfuPfvQjo+aidlA4dw6///77jf6lix0Z3CjxT23QtMqsaf1eHzx4cLhhohqU6Wbasf2eT98wUg1f7a/6fK5XdCMg3T+kHSyu3mqlnBdb3Dv1/FPKN0+HHTRtADosYOxu3ALqh0UXUL6ceeaZrpaVf67H9AXZPPPMk15UdzrdF8nsSgSqLxT93aWWWioMGKFBJFZYYQVjR5gLTSTq7nAJZ3Zq3FUD6Nhjj3UR0w9G+g7r2pu3E+Ou84+vBaKjVd+i6mB8zJgxZrfddnPP1Vfkvffea9SJtYr6rNMgApRZAp0adx8/xVl9g5100kmu1oNqDemfaoIff/zxoX9IO/K9q1XiX1f2x06Me6OYaTAo3xRw++23dwMmNFq37PM7Oe5XXnml+d73vueSgIqjkn3q/iHdhFRdQKjlih0tuuyhrjh+3SjZbrvt3GB6WuC70UivFNvveTVb9gNPaUAwtfaoV9NPg4OoG4h0SScF0/PLNh1b3Dv5/FO2904nHi+JwE6MGvscpYDu2Pz0pz91F1jaQSVY9tprr5p9TfcVpYuxViW9TjuJw1bba2f5qFGjXK0QJf9UY0gjR6qmmL4gdXGYTna2s70ir9OpcVcs1R/gzJkzzaqrrlrzo7DIMcvi2Do17qrVp5HF9Zm+5JJLzOjRo0OyP+2iEQc1aqUvSgT6kUX9vDI+dmrc06OB6jtF53HVAksPCqPp4447zt3sUWy13iGHHFLGMNccc6fGveZA7Ax1B6HaQL5QG9BL1D52ctx1jtd3vAaA6tOnj6v1p8SV+hNTElg3hJToUtF3gvp+fv3112sRSjhHPptuuqm7IabDVx+uulmSbqGj+TH+ntf3tq9AMH78eDdQ1QknnOC69dENANUGV7JQ53fV9veF1iCz+kmMKe6dfP7x7yse4xYgERh3fNi7DhHQl6y+XJVUUdHF1Iknnlh379URvy/qi6tVSa+zwAILtFo9k+X6QeAvELW/w4cPd81JfNNADR6iPsTKXjo57uokXoMS6Ifs5ZdfbmZXkrkI75lOjrsuEFTzU59p1RZoVlRTyHcer3Obag6WuXRy3NPfOxpNUjWAGxXV+u7bt69b/MADD1Q0H2v0miLP7+S414uL+oecOnWqW6TRqn3T8HrrlnleJ8ddtcAOP/xw13pDTb8nTJjgPvNKCPqy/PLLu+TQ/vvv72apH1ENOlf2osE/1E2GulBQUR/f6iIj3de3N0qfV9O/1f3y6sf0Onn9ntfIv9dcc02o9akb+qrpre9zfe8ruaT3gUaUVr/fvuS1P377sT/GFvdOPv/EHmv27ysBEoFfWTCFQJcFdEdNtf70JaviR97zz93Mqv/Ub6Av6Q5e/bzqx/Q66ddWr5f3czUvUTNCX5QMLGvp9LjrokBNQVVGjhzpfuDqwqH6X7oWkd6HfrmOv4yl0+PenZgpUeBLeuRZP68Mj92Jux+tUz7pc3gjr/Q6eZzn0/ujPq78jZ56+6OLRD9wxKefflrRlLze+kWdV4S414vN2LFjw+xddtklTDMxS6AIcdfNPd9/nX6jqrZfo6J+gvv16+cW/+UvfwkD3TVav8jzNVKz+lX0IwGvueaaLiFYLwkoh/R5NX0Ob2SUXieP87z/uxogRt/XGgQoPeCgksLqL/Luu+92tQLVDZAvZU4ExhT37px/0u+l9HvMx7b6Mb1O+rXV6/G8+AIkAosfY44wJwGdSLfaaitz8cUXu78w77zzurur++67b9O/qH72fNGdulbFrzPXXHOFO3ytXpPX8g033DBsOj1cfZhZgokixF2DwfiiJoILLbRQ3X8aMdYX9T3j11MtobKVIsS9OzFTkyhffJ9i/nkZHrsbdzUh8xfX/hzezCu9ju+bsdn6XV2m/l59aXRR65frUf2I+eIviv3zMjwWJe7VsdIAQP68rqTAzjvvXL1KqZ8XJe7PPvtsiKOaOjYr+u263nrruVXUT1xZa35ff/31rrb8u+++6yz0+1595TYb9T2283w6zhrYTyNBq6/AadOmub4OdTNXN4J9TUA/arBuDKmGaBlLTHHv7vmnk68ry/iei+mYSQTGFA32pWMEdEGsO6y3336722d94eoHQ7q/jUYHo/7YfGl1gaXmeP6LWqOBZd18U30FqTmI+oe57bbb/G41fEz/INLIkmUrRYl72eLW0+MtUtx1t/mtt94yTz75pPn3v//dksaff7SiOksvU+lJ3OXkz/X6ce8vLhv5pW+s+Iu0Rut2Z356NEl1CdCqpEeQ1A2AMpUixb06burSww8KoZpP6UR/9bple16kuKfP7f6GRLN4ppNA6lOwbOWPf/yj2WGHHVw/2Dp2NZfWDVMlSVuVmM7zjfZ16aWXdn0Fpmt/6fpCvwNUVltttYrajY22U7T5McW9J+cf/x5UfL7u68qivUeKfjxzFf0AOT4EshbQHbXNN988jCSmJrO33HJLzejAjf6umtqpny41ubrvvvsarebmT5w40a2nJ+uss07TdbuzUPugLxA96mJPdw5V87BRefrpp8Mi33QszCj4RJHiPnjw4NAHWLOwqeaIrx2w3377hdcsu+yyzV5WqGVFirsCo4EBTj/9dBcjdWGg/kybFR9/rfPd73632aqFWtbTuAtD52zdZFHRuV6DSTUq999/f1iUx7k+vU3f91X4g3Um0onJdmoQ1tlER84qWtyrg5CuzZ1+T1SvV7bnRYt7//79Qwj1OzLdmiMsSE2kR5Iv03leBOozT4k/1ZZVLdnf/e53Fd3gpJjqTsZ0ntf7WKPMagR43fxRM+BG5YYbbggjh/saoY3WLeL8mOLe0/NPTNeVRXyvFPqY7ImPggACXRCwTWkSe1Jw/2zNjcTeee3Cq2etakdqC9t4/PHHG75+n332CevZjn0brldvgW0K5l5rO32vtzjMs6OHtf03bAIprHv++eeHbZRhomhxbydm6WO2tYjaeUnh1kkbFOHzbmsxh8+wTeYntlZww5jdddddYV07wERiaxM2XLdoC7KI+6RJk4KfzvmNiq11mdhBe9y6a621VqPV6s5v9zyvF6+yyiphf2yfSHW3p5na7znnnNOta7sEaLheERcUMe7pONk+w8J7wI4gml5U6ulOibsdCTjE7+yzz24YM1ubLayn33jNiu1LMLGtPdz6dkC4xPYN3Gz1Qi2z/egl9ua3O3ad8y655JIuH9/sOM+3G3c7KnSiGOoaxdb4bXosW2yxRXiPNPs+aLqRDl0YW9yzOP/MjuvKDg03u91EQHdAKAgg0KaArd0Rvjhtc+DENrFr85WVq9k7dmE7Si7YZliVK9hn48ePDxeHttZh0wv2mhfbGe1eIJ577rlhX5ZbbrlEPwrrlfR6tjlR3X2u97oizCti3NuJS/rHSRkTgUWM+yeffJIsssgi4TM/evToum8F2zysInE0bty4uusVcWZWcZeN7WzeWduaJslll11WwzV9+vRk0KBBIR52tMeadZrNaPc8r22kz+G68LcdxddsWu8PWzsk7E+ZbvgUNe7pIOu729/ItDXF0otKO91Jce9KQki/53ysbU3wuvHVjaA99tgjrDds2LC66xV15vrrrx+O3Y7S2u3DzPs8327cdQCbbLKJOyZ95+hmXr1y0kknheO2A4vUW6XQ82KKe1bnn9lxXVnoN0VJD24OHbf9oqAggEALAY2gqn40nnvuObem+staaaWVWrxq1mJ7580ceOCBFeuqeebf/vY3N0/NbO0FuRutTH27XHvttebwww836sPD3qV0fZVsvfXWFa9v9UR9grz++uuuOWez/ql0ClCnyH5f1FT4jDPOMPaL0tg7i0adTuu5qtGraH/sj4uWTU3cygX4r6hxbyc0Q4cONVdddZVbVf2K2Vph7bysEOsUOe7qykD9meqzr8+zmkVp9Gh74ej6stOIgocccohrXqRg6vxgb0wUIq6tDiLruPvRCL31iSeeaDRSq03gmUceecQ11fZdRAwcONCo6aZi0m5p9zyv7WkfNtpoo9AlhT7Phx56qHsv2JrjRk0IDzroIPPEE0+4P6/mYur7tiv70+5+x7ZekePurW3SOfR5piaQtvZQeO7XKdtjp8X9oosuMiNGjHBhsjUCzS9+8YuGIdN5ZeONNzbqC1pFr9Mosho1XEWf86OOOsp9xvVcXX6oz7h0P3KaX9Ry6aWXGpv4dIenc5ya0epz0U6xNQcrBu/L+zzflbir73Idi+KuEYPPPPNMM2TIEGNrnbsRkPWb7rzzznOHufDCC7vvnDI1B48p7lmff/K+rmzns8E6HSZgfxhSEECgDQGbKAt30OzHvEvTw4cPr/kLalJs+xqs2I5vHpbevv0Sr3ltOzO6UlPEJgoTm+Ss2Bf7wyg0MfD7Y38gJmVrTlTkuLd6H5W5RmDR437qqacmdqTAis+8HYyo4rk+9/YGRmJvSLR6qxRmedZxF4wduTGxnc5X2Faf6+0ovYnOw10tXTnPa9t2kKdEtX78Od0/Vu+Pvg/sSMZd3Z2OXb/ocVdgbD9wIe52cIiOjVWWO95pce9KzTA5nXPOOUmfPn1C3PV512ddv+/8Z1+PdtTRxA4kkyVt9NuyN14qDNIerabrnRvzPM93Ne6279+KY1PtQN9k2B+b7Rc8adY1UfQB7OYOxhT3rM8/eV9XdpOcl0Us0P5tZ3vmoCBQZoH0QBlZOOhO3IQJE9wdWU2rqAagL+roV7VwVDMn76LaII899pi7c7jAAgu4P6e7ibpbpWJ/OLqO7jVwwJ577unmleW/Ise9LDHsznEWPe5HHnmksRcBrsaI95kxY4ab1IBBqv2sWg9nnXVW0wGE/GuL8ph13OWiWjiqMWL7/zM2+eqo/Lleta51jtdynYfzLjYp4Gp32wtLV6Pd14Dx+7Pgggu62qEPPvigq7WY9/7Esv2ix13OGi3cFw1yRjGm6HFXbW8do2qA+4Hg9Fn3tQTnm28+9xt0ypQp7vxUpvdEeiCsLI47pvO8BgLTqMe+1ZLNQ4Tf8xqsUDVJVftzjTXWyOLQO2obMcU96/NPTNeVHfWmKPHO0jS4xMHn0OMSmDp1qrGdDpvevXsbNTvWSI1fR5Ms/UB8+eWXXZNg21+U0ehz2h8lAynZC8QS9+yPjC02E4gp7h988IHr8kAjR6p5mLoq0HmIkr2AmmeqSd60adOMrZXlmtv7my/Z/7XWW7R9wrqEsEaMV+yVAFaykJKtQGxxz/bo2FojgZjirhu7Gg1c3b0oAahuYNS1gL8Z0OgYmN91gVjirt/zdjAq9/2uc72tee5+z88///xdPyhe0VIglrj7HY3pd6bfJx7jEiARGFc82BsEEEAAAQQQQAABBBBAAAEEEEAAAQRyEaBpcC6sbBQBBBBAAAEEEEAAAQQQQAABBBBAAIG4BEgExhUP9gYBBBBAAAEEEEAAAQQQQAABBBBAAIFcBEgE5sLKRhFAAAEEEEAAAQQQQAABBBBAAAEEEIhLgERgXPFgbxBAAAEEEEAAAQQQQAABBBBAAAEEEMhFgERgLqxsFAEEEEAAAQQQQAABBBBAAAEEEEAAgbgESATGFQ/2BgEEEEAAAQQQQAABBBBAAAEEEEAAgVwESATmwspGEUAAAQQQQAABBBBAAAEEEEAAAQQQiEuARGBc8WBvEEAAAQQQQAABBBBAAAEEEEAAAQQQyEWARGAurGwUAQQQQAABBBBAAAEEEEAAAQQQQACBuARIBMYVD/YGAQQQQAABBBBAAAEEEEAAAQQQQACBXARIBObCykYRQAABBBBAAAEEEEAAAQQQQAABBBCIS4BEYFzxYG8QQAABBBBAAAEEEEAAAQQQQAABBBDIRYBEYC6sbBQBBBBAAAEEEEAAAQQQQAABBBBAAIG4BEgExhUP9gYBBBBAAAEEEEAAAQQQQAABBBBAAIFcBEgE5sLKRhFAAAEEEEAAAQQQQAABBBBAAAEEEIhLgERgXPFgbxBAAAEEEEAAAQQQQAABBBBAAAEEEMhFgERgLqxsFAEEEEAAAQQQQAABBBBAAAEEEEAAgbgESATGFQ/2BgEEEEAAAQQQQAABBBBAAAEEEEAAgVwESATmwspGEUAAAQQQQAABBBBAAAEEEEAAAQQQiEuARGBc8WBvEEAAAQQQQAABBBBAAAEEEEAAAQQQyEWARGAurGwUAQQQQAABBBBAAAEEEEAAAQQQQACBuARIBMYVD/YGAQQQQAABBBBAAAEEEEAAAQQQQACBXARIBObCykYRQAABBBBAAAEEEEAAAQQQQAABBBCIS4BEYFzxYG8QQAABBBBAAAEEEEAAAQQQQAABBBDIRYBEYC6sbBQBBBBAAAEEEEAAAQQQQAABBBBAAIG4BEgExhUP9gYBBBBAAAEEEEAAAQQQQAABBBBAAIFcBEgE5sLKRhFAAAEEEEAAAQQQQAABBBBAAAEEEIhLgERgXPFgbxBAAAEEEEAAAQQQQAABBBBAAAEEEMhFgERgLqxsFAEEEEAAAQQQQAABBBBAAAEEEEAAgbgESATGFQ/2BgEEEEAAAQQQQAABBBBAAAEEEEAAgVwESATmwspGEUAAAQQQQAABBBBAAAEEEEAAAQQQiEuARGBc8WBvEEAAAQQQQAABBBBAAAEEEEAAAQQQyEWARGAurGwUAQQQQAABBBBAAAEEEEAAAQQQQACBuARIBMYVD/YGAQQQQAABBBBAAAEEEEAAAQQQQACBXARIBObCykYRQAABBBBAAAEEEEAAAQQQQAABBBCIS4BEYFzxYG8QQAABBBBAAAEEEEAAAQQQQAABBBDIRYBEYC6sbBQBBBBAAAEEEEAAAQQQQAABBBBAAIG4BEgExhUP9gYBBBBAAAEEEEAAAQQQQAABBBBAAIFcBEgE5sLKRhFAAAEEEEAAAQQQQAABBBBAAAEEEIhLgERgXPFgbxBAAAEEEEAAAQQQQAABBBBAAAEEEMhFgERgLqxsFAEEEEAAAQQQQAABBBBAAAEEEEAAgbgESATGFQ/2BgEEEEAAAQQQQAABBBBAAAEEEEAAgVwESATmwspGEUAAAQQQQAABBBBAAAEEEEAAAQQQiEuARGBc8WBvEEAAAQQQQAABBBBAAAEEEEAAAQQQyEWARGAurGwUAQQQQAABBBBAAAEEEEAAAQQQQACBuARIBMYVD/YGAQQQQAABBBBAAAEEEEAAAQQQQACBXARIBObCykYRQAABBBBAAAEEEEAAAQQQQAABBBCIS4BEYFzxYG8QQAABBBBAAAEEEEAAAQQQQAABBBDIRYBEYC6sbBQBBBBAAAEEEEAAAQQQQAABBBBAAIG4BEgExhUP9gYBBBBAAAEEEEAAAQQQQAABBBBAAIFcBEgE5sLKRhFAAAEEEEAAAQQQQAABBBBAAAEEEIhLgERgXPFgbxBAAAEEEEAAAQQQQAABBBBAAAEEEMhFgERgLqxsFAEEEEAAAQQQQAABBBBAAAEEEEAAgbgESATGFQ/2BgEEEEAAAQQQQAABBBBAAAEEEEAAgVwESATmwspGEUAAAQQQQAABBBBAAAEEEEAAAQQQiEuARGBc8WBvEEAAAQQQQAABBBBAAAEEEEAAAQQQyEWARGAurGwUAQQQQAABBBBAAAEEEEAAAQQQQACBuARIBMYVD/YGAQQQQAABBBBAAAEEEEAAAQQQQACBXATmymWrbBQBBBBAAAEEEECgYwUefvhh8+c//9nt/w9/+EMzfPjwpscybdo0c8opp7h11ltvPbPbbrvVrP/EE0+YsWPHGj1OnjzZzD333Ob73/+++zdixAizxBJL1LymesYXX3xhLr/8creNf/zjH0b/pk+fblZccUWz8soru23tt99+btvVr73nnnvMVVdd5Wafdtpp5v333zd6HD9+vJl33nnN5ptvbkaOHGmWWWaZ6pfyHAEEEEAAAQQQKIzAHIkthTkaDgQBBBBAAAEEEECgxwL//Oc/zZJLLmk+//xz07dvX/Pmm2+ab3zjGw23e+KJJ5rjjjvOLR83bpzZbrvtwrpffvmlS7gdf/zxZubMmWF+emLRRRc1F1xwQcXr0ss1/eyzz5phw4aZRx99tHpRxfPvfOc75tJLLzVrr712xfwxY8aYAw44wM1TIvLHP/6xefXVVyvWueiii8xee+1VMY8nCCCAAAIIIIBAkQRIBBYpmhwLAggggAACCCCQkcCQIUPMDTfc4LZ20003mW222abhlldaaSXz4osvGiX03njjjYqk4dZbb21uueUW99revXuboUOHupp7M2bMMBMnTjRKHCrhqHLGGWeYQw891E2n//v73/9uVNPw008/dbM32mgjs9lmm5nFF1/cvPfee+aZZ54xV155ZViu2oFKHM4551e94KQTgYMGDTK3336721avXr2Mahr26dPHvP3222b++edP/2mmEUAAAQQQQACBQgnQNLhQ4eRgEEAAAQQQQACBbAT23HPPkAhUc9xGicAHH3zQJQH1V3fdddeKJKCa4vok4Oqrr26uueYao6Rhutx3331mxx13NKqFeOyxx5qddtrJ9OvXL72KSxD6JOA555xj9t9//4rlenLCCSeYgQMHmrfeess8//zz7u822mclAVdYYQXzhz/8wSUUVUPwlVdeIQlYo8oMBBBAAAEEECiawFe3SYt2ZBwPAggggAACCCCAQLcFttpqK7PYYou51994443mww8/rLstNcP1ZY899vCTru++UaNGueeqCXj11VfXJAG1cIMNNjCXXXaZW+/jjz82RxxxhJv2/6lZsvoWVFGir14SUMvUt9/BBx+sSVfUF2GjopqCl1xyiVFtRfVVqGbEP/vZzxqtznwEEEAAAQQQQKAwAiQCCxNKDgQBBBBAAAEEEMhOQH0CqoafyieffGKuvfbamo2rlp4SfCqq8ad/vqg58Wuvveae/vznP3eDefhl1Y8aqKN///5utpr4fvbZZ2GV+eabz9x6662uD8HTTz89zK83oSbBviip2KisueaaZt111220mPkIIIAAAggggEBhBUgEFja0HBgCCCCAAAIIINAzATUP9kXNg6uLkn3qo08lXRtQz9U815d2km5+cA/11zd16lT/UvOtb33LbLzxxkYjC6+//vphfnpCfQw+9NBDZsKECWG273cwzEhNaJRhCgIIIIAAAgggUEYB+ggsY9Q5ZgQQQAABBBBAoA2BAQMGGNWee+yxx8zdd9/tBgLRaMK++Ca96dqDftkLL7zgJ83hhx9ujjrqqPC83sQHH3wQZuu1Gv23XtG+TJo0yWgdJRv1TwOVpGsR6nVJktR7uZtX3U9hwxVZgAACCCCAAAIIFEyARGDBAsrhIIAAAggggAACWQqoVqCSb19++aUbmVdJPZV333031MBTf4IaMThdlJzzRet2pbz00ks1q6tG4m9+8xszZcqUmmWaoWTk8ssvb5577rm6y9Mzl1pqqfRTphFAAAEEEEAAgdIIkAgsTag5UAQQQAABBBBAoOsCu+yyixk5cqRRf4BKxvlEoEYEnjlzptvgsGHDajY877zzhnnHH3+8WWSRRcLzVhPVTYlPPvlkc8wxx4SX9erVy6h572qrrRb6JtSgIxqBeNttt3XrzTHHHGH96gkNFkJBAAEEEEAAAQTKKEAisIxR55gRQAABBBBAAIE2BRZaaCGz3XbbuUFBJk+e7JrialAOJQJV+vbta7bZZpuarWmdO+64w81X/3+DBw+uWaedGXfeeWdIAs4111zm1FNPNXvvvbdZYIEFal6ebl6sGowUBBBAAAEEEEAAgUoBbodWevAMAQQQQAABBBBAoEogPWjIuHHjXLPghx9+2K2lGoNqlltd0iP4Pv7449WLa56r379HHnnEvPPOOxX9+2lAEl9Us1A1EuslAbXOtGnT/Kqm2WAhYSUmEEAAAQQQQACBkgmQCCxZwDlcBBBAAAEEEECgqwKDBg0yfpCQ66+/3tx8882uz0Btp3q0YL/tgQMH+klzzjnnmA8//DA8r5745JNPXI1BvWaJJZYwzz77bFhFyUFfNt10Uz9Z86jBQsaOHRvma/RhCgIIIIAAAggggEClAInASg+eIYAAAggggAACCFQJqE++3Xff3c1VYu7888930+qjb4011qhae9bTddZZx+y4447uyZtvvmkOO+ww0yg5N2rUKFcTUCtvttlmZtVVV521Efu/T0BqxpNPPhnmpye03aFDh7pBTfz8GTNm+EkeEUAAAQQQQAABBP4vQCKQtwICCCCAAAIIIIBASwFf8y9JEuObBdcbJCS9odGjR5s+ffq4WRdeeKHZcMMNzT333GPUl5+Sd0899ZRR0+Kzzz7braN1TznllPQmzMYbbxyeH3HEEeass84yU6dOdfP+9a9/GdVQHDJkiLnuuuvCeprQMgoCCCCAAAIIIIBApcAc9sdcUjmLZwgggAACCCCAAAII1Aqst9565oEHHnALNHDH66+/bhZffPHaFVNz7rrrLqM+BtP992lEX9UyTPfjp34Gb7zxRrPlllumXm1cE2QlAzUicLr069fPvPXWW6E/wf79+5vzzjvP7LDDDq52oWoS6m/6EYLHjBljDjjgALcJJSWHDx+e3hzTCCCAAAIIIIBAKQSoEViKMHOQCCCAAAIIIIBAzwXSg4ZoFOBWSUD9xU022cRMmTLF7LvvvkYjEKvoPrRPAvpmx08//XRNElDrKpGnPgmPPPJIM88882iWK2purO2oT0GNJKzBRpSoVH+GKm+88Yarfeie8B8CCCCAAAIIIICAE6BGIG8EBBBAAAEEEEAAgbYErrjiCrPbbru5ddUUd/vtt2/rdemVlMBTYvDjjz82yy+/vPs3//zzp1dpOP3RRx+ZF1980bz00ktm4YUXNgMGDDB9+/ZtuD4LEEAAAQQQQAABBCoFSARWevAMAQQQQAABBBBAoIGAatvdcccdribga6+9ZtScl4IAAggggAACCCDQOQI0De6cWLGnCCCAAAIIIIDA1ybwzDPPmDvvvNP9fQ0cQhLwawsFfxgBBBBAAAEEEOi2ADUCu03HCxFAAAEEEEAAgeIKvPfee2503wUXXNBMnDjRjBgxwg2+8c1vftO8/PLLRoNxUBBAAAEEEEAAAQQ6S2Cuztpd9hYBBBBAAAEEEEBgdgioH78NNtig5k9pYA6SgDUszEAAAQQQQAABBDpCgKbBHREmdhIBBBBAAAEEEJi9Assss0zNH9x7773NYYcdVjOfGQgggAACCCCAAAKdIUDT4M6IE3uJAAIIIIAAAgjMVoEvvvjCqPbf5MmT3ci+W265pash2KtXr9m6H/wxBBBAAAEEEEAAgewESARmZ8mWEEAAAQQQQAABBBBAAAEEEEAAAQQQiFaApsHRhoYdQwABBBBAAAEEEEAAAQQQQAABBBBAIDsBEoHZWbIlBBBAAAEEEEAAAQQQQAABBBBAAAEEohUgERhtaNgxBBBAAAEEEEAAAQQQQAABBBBAAAEEshMgEZidJVtCAAEEEEAAAQQQQAABBBBAAAEEEEAgWgESgdGGhh1DAAEEEEAAAQQQQAABBBBAAAEEEEAgOwESgdlZsiUEEEAAAQQQQAABBBBAAAEEEEAAAQSiFSARGG1o2DEEEEAAAQQQQAABBBBAAAEEEEAAAQSyEyARmJ0lW0IAAQQQQAABBBBAAAEEEEAAAQQQQCBaARKB0YaGHUMAAQQQQAABBBBAAAEEEEAAAQQQQCA7ARKB2VmyJQQQQAABBBBAAAEEEEAAAQQQQAABBKIVIBEYbWjYMQQQQAABBBBAAAEEEEAAAQQQQAABBLITIBGYnSVbQgABBBBAAAEEEEAAAQQQQAABBBBAIFoBEoHRhoYdQwABBBBAAAEEEEAAAQQQQAABBBBAIDsBEoHZWbIlBBBAAAEEEEAAAQQQQAABBBBAAAEEohUgERhtaNgxBBBAAAEEEEAAAQQQQAABBBBAAAEEshMgEZidJVtCAAEEEEAAAQQQQAABBBBAAAEEEEAgWgESgdGGhh1DAAEEEEAAAQQQQAABBBBAAAEEEEAgOwESgdlZsiUEEEAAAQQQQAABBBBAAAEEEEAAAQSiFSARGG1o2DEEEEAAAQQQQAABBBBAAAEEEEAAAQSyEyARmJ0lW0IAAQQQQAABBBBAAAEEEEAAAQQQQCBaARKB0YaGHUMAAQQQQAABBBBAAAEEEEAAAQQQQCA7ARKB2VmyJQQQQAABBBBAAAEEEEAAAQQQQAABBKIVIBEYbWjYMQQQQAABBBBAAAEEEEAAAQQQQAABBLITIBGYnSVbQgABBBBAAAEEEEAAAQQQQAABBBBAIFoBEoHRhoYdQwABBBBAAAEEEEAAAQQQQAABBBBAIDsBEoHZWbIlBBBAAAEEEEAAAQQQQAABBBBAAAEEohUgERhtaNgxBBBAAAEEEEAAAQQQQAABBBBAAAEEshMgEZidJVtCAAEEEEAAAQQQQAABBBBAAAEEEEAgWgESgdGGhh1DAAEEEEAAAQQQQ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/>
<!-- rnb-plot-end -->
<!-- rnb-source-begin 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 -->
```r
```r
emr_tot <- tot_world %>% filter(g_whoregion == \EMR\) %>% select(newrel_mtot, newrel_ftot) %>%
t()
colnames(emr_tot) <- c(\2013\, \2014\, \2015\, \2016\, \2017\, \2018\, \2019\, \2020\)
emr_tot %<>% barplot(col = c(\skyblue\, \seagreen1\), beside = TRUE, ylim = c(0,500000), xlab = \year\, ylab = \notifications\) + title(\Sex disaggregated notifications for all ages years in EMR\)
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVtZXJpYygwKVxuIn0= -->
numeric(0)
<!-- rnb-output-end -->
<!-- rnb-plot-begin -->
<img 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" />
<!-- rnb-plot-end -->
<!-- rnb-source-begin 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 -->
```r
```r
eur_tot <- tot_world %>% filter(g_whoregion == \EUR\) %>% select(newrel_mtot, newrel_ftot) %>%
t()
colnames(eur_tot) <- c(\2013\, \2014\, \2015\, \2016\, \2017\, \2018\, \2019\, \2020\)
eur_tot %<>% barplot(col = c(\skyblue\, \seagreen1\), beside = TRUE, ylim = c(0,200000), xlab = \year\, ylab = \notifications\) + title(\Sex disaggregated notifications for all ages years in EUR\)
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVtZXJpYygwKVxuIn0= -->
numeric(0)
<!-- rnb-output-end -->
<!-- rnb-plot-begin -->
<img 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" />
<!-- rnb-plot-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuc2VhX3RvdCA8LSB0b3Rfd29ybGQgJT4lIGZpbHRlcihnX3dob3JlZ2lvbiA9PSBcXFNFQVxcKSAlPiUgc2VsZWN0KG5ld3JlbF9tdG90LCBuZXdyZWxfZnRvdCkgJT4lIFxuICB0KCkgXG5jb2xuYW1lcyhzZWFfdG90KSA8LSBjKFxcMjAxM1xcLCBcXDIwMTRcXCwgXFwyMDE1XFwsIFxcMjAxNlxcLCBcXDIwMTdcXCwgXFwyMDE4XFwsIFxcMjAxOVxcLCBcXDIwMjBcXClcbnNlYV90b3QgJTw+JSBiYXJwbG90KGNvbCA9IGMoXFxza3libHVlXFwsIFxcc2VhZ3JlZW4xXFwpLCBiZXNpZGUgPSBUUlVFLCB5bGltID0gYygwLDMwMDAwMDApLCB4bGFiID0gXFx5ZWFyXFwsIHlsYWIgPSBcXG5vdGlmaWNhdGlvbnNcXCkgKyB0aXRsZShcXFNleCBkaXNhZ2dyZWdhdGVkIG5vdGlmaWNhdGlvbnMgZm9yIGFsbCBhZ2VzIHllYXJzIGluIFNFQVxcKVxuYGBgXG5gYGAifQ== -->
```r
```r
sea_tot <- tot_world %>% filter(g_whoregion == \SEA\) %>% select(newrel_mtot, newrel_ftot) %>%
t()
colnames(sea_tot) <- c(\2013\, \2014\, \2015\, \2016\, \2017\, \2018\, \2019\, \2020\)
sea_tot %<>% barplot(col = c(\skyblue\, \seagreen1\), beside = TRUE, ylim = c(0,3000000), xlab = \year\, ylab = \notifications\) + title(\Sex disaggregated notifications for all ages years in SEA\)
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVtZXJpYygwKVxuIn0= -->
numeric(0)
<!-- rnb-output-end -->
<!-- rnb-plot-begin -->
<img 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" />
<!-- rnb-plot-end -->
<!-- rnb-source-begin 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 -->
```r
```r
wpr_tot <- tot_world %>% filter(g_whoregion == \WPR\) %>% select(newrel_mtot, newrel_ftot) %>%
t()
colnames(wpr_tot) <- c(\2013\, \2014\, \2015\, \2016\, \2017\, \2018\, \2019\, \2020\)
wpr_tot %<>% barplot(col = c(\skyblue\, \seagreen1\), beside = TRUE, ylim = c(0,1500000), xlab = \year\, ylab = \notifications\) + title(\Sex disaggregated notifications for all ages years in WPR\)
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVtZXJpYygwKVxuIn0= -->
numeric(0)
<!-- rnb-output-end -->
<!-- rnb-plot-begin -->
<img 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```r
##Attempt at creating a forecast
filt_afr04 <- perc_world %>% filter(g_whoregion == "AFR") %>% filter(year != 2020)
ts_afr04 <- ts(filt_afr04[, c(3)], start = 2013, frequency = 1)
arima_afr04 <- auto.arima(ts_afr04, d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
ARIMA(0,1,0) : 111.1694
ARIMA(0,1,0) with drift : 114.7184
ARIMA(0,1,1) : 115.9016
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 115.7354
ARIMA(1,1,0) with drift : Inf
ARIMA(1,1,1) : 125.4818
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) print(summary(arima_afr04))Series: ts_afr04
ARIMA(0,1,0)
sigma^2 estimated as 3954567: log likelihood=-54.08
AIC=110.17 AICc=111.17 BIC=109.96
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 793.0733 1841.094 1358.216 3.037032 5.197868 0.8590867 -0.650155checkresiduals(arima_afr04)
Ljung-Box test
data: Residuals from ARIMA(0,1,0)
Q* = 5.0245, df = 3, p-value = 0.17
Model df: 0. Total lags used: 3
fcst_afrm04 <- forecast(arima_afr04, h = 1)
autoplot(fcst_afrm04)
sum_afrm04 <- print(summary(fcst))
Forecast method: ARIMA(0,1,0)
Model Information:
Series: ts_afr04
ARIMA(0,1,0)
sigma^2 estimated as 3954567: log likelihood=-54.08
AIC=110.17 AICc=111.17 BIC=109.96
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 793.0733 1841.094 1358.216 3.037032 5.197868 0.8590867 -0.650155
Forecasts:####Attempt at creating function to produce predictions
out_2020 <- function(df = tot_world, region){
df %>% filter(year != 2020) %>% filter(g_whoregion == region) %>% return() }
arima_TB <- function(df = tot_world, group) {
data1 <- df %>% select(group) %>% ts(start = 2013, frequency = 1) %>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
print(summary(data1))
FCST <- forecast(data1, h = 1)
autoplot(FCST)
print(summary(FCST))}
##AFRICA ESTIMATES
afr_m04 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m04") Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(group)` instead of `group` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.
ARIMA(0,1,0) : 111.1694
ARIMA(0,1,0) with drift : 114.7184
ARIMA(0,1,1) : 115.9016
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 115.7354
ARIMA(1,1,0) with drift : Inf
ARIMA(1,1,1) : 125.4818
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 3954567: log likelihood=-54.08
AIC=110.17 AICc=111.17 BIC=109.96
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 793.0733 1841.094 1358.216 3.037032 5.197868 0.8590867 -0.650155
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 3954567: log likelihood=-54.08
AIC=110.17 AICc=111.17 BIC=109.96
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 793.0733 1841.094 1358.216 3.037032 5.197868 0.8590867 -0.650155
Forecasts:rownames(afr_m04) <- "afr_m04"
afr_f04 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f04")
ARIMA(0,1,0) : 111.1038
ARIMA(0,1,0) with drift : 114.5687
ARIMA(0,1,1) : 116.0674
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 116.0417
ARIMA(1,1,0) with drift : 123.1058
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 3911516: log likelihood=-54.05
AIC=110.1 AICc=111.1 BIC=109.9
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 808.0217 1831.046 1337.165 3.504448 6.329902 0.8588083 -0.4289628
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 3911516: log likelihood=-54.05
AIC=110.1 AICc=111.1 BIC=109.9
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 808.0217 1831.046 1337.165 3.504448 6.329902 0.8588083 -0.4289628
Forecasts:rownames(afr_f04) <- "afr_f04"
afr_m514 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m514")
ARIMA(0,1,0) : 117.6737
ARIMA(0,1,0) with drift : 121.1402
ARIMA(0,1,1) : 122.6736
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 122.6736
ARIMA(1,1,0) with drift : 130.0079
ARIMA(1,1,1) : 132.6046
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 131.7788
ARIMA(2,1,0) with drift : 159.9311
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 11691922: log likelihood=-57.34
AIC=116.67 AICc=117.67 BIC=116.47
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1395.087 3165.699 2467.373 4.502111 8.326399 0.8582664 -0.3285392
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 11691922: log likelihood=-57.34
AIC=116.67 AICc=117.67 BIC=116.47
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1395.087 3165.699 2467.373 4.502111 8.326399 0.8582664 -0.3285392
Forecasts:rownames(afr_m514) <- "afr_m514"
afr_f514 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f514")
ARIMA(0,1,0) : 117.2334
ARIMA(0,1,0) with drift : 121.2201
ARIMA(0,1,1) : 122.2328
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 122.2325
ARIMA(1,1,0) with drift : 130.5247
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 131.8107
ARIMA(2,1,0) with drift : 160.4149
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 10864754: log likelihood=-57.12
AIC=116.23 AICc=117.23 BIC=116.03
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1117.231 3051.663 2274.088 3.379023 7.511132 0.8584703 -0.2394624
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 10864754: log likelihood=-57.12
AIC=116.23 AICc=117.23 BIC=116.03
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1117.231 3051.663 2274.088 3.379023 7.511132 0.8584703 -0.2394624
Forecasts:rownames(afr_f514) <- "afr_f514"
afr_m014 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m014")
ARIMA(0,1,0) : 121.8118
ARIMA(0,1,0) with drift : 125.8302
ARIMA(0,1,1) : 126.7857
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 126.7703
ARIMA(1,1,0) with drift : 134.2116
ARIMA(1,1,1) : 136.3851
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 135.8829
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 23303291: log likelihood=-59.41
AIC=120.81 AICc=121.81 BIC=120.6
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1614.328 4469.256 3188.614 2.684558 5.574168 0.8590016 -0.3180155
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 23303291: log likelihood=-59.41
AIC=120.81 AICc=121.81 BIC=120.6
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1614.328 4469.256 3188.614 2.684558 5.574168 0.8590016 -0.3180155
Forecasts:rownames(afr_m014) <- "afr_m014"
afr_f014 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f014")
ARIMA(0,1,0) : 122.4939
ARIMA(0,1,0) with drift : 126.8435
ARIMA(0,1,1) : 127.4896
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 127.488
ARIMA(1,1,0) with drift : 136.419
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 137.1726
ARIMA(2,1,0) with drift : 166.4068
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 26109056: log likelihood=-59.75
AIC=121.49 AICc=122.49 BIC=121.29
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1410.503 4730.665 3787.646 2.301644 7.442093 0.8586494 -0.1756356
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 26109056: log likelihood=-59.75
AIC=121.49 AICc=122.49 BIC=121.29
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1410.503 4730.665 3787.646 2.301644 7.442093 0.8586494 -0.1756356
Forecasts:rownames(afr_f014) <- "afr_f014"
afr_m15plus <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m15plus")
ARIMA(0,1,0) : 147.8467
ARIMA(0,1,0) with drift : 151.7651
ARIMA(0,1,1) : 152.7979
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 152.7548
ARIMA(1,1,0) with drift : 159.8714
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 161.2934
ARIMA(2,1,0) with drift : 189.8465
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 1.786e+09: log likelihood=-72.42
AIC=146.85 AICc=147.85 BIC=146.64
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 14791.42 39126.57 26895.42 2.246912 4.075435 0.8596953 -0.3986632
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 1.786e+09: log likelihood=-72.42
AIC=146.85 AICc=147.85 BIC=146.64
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 14791.42 39126.57 26895.42 2.246912 4.075435 0.8596953 -0.3986632
Forecasts:rownames(afr_m15plus) <- "afr_m15plus"
afr_f15plus <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f15plus")
ARIMA(0,1,0) : 143.4497
ARIMA(0,1,0) with drift : 148.2692
ARIMA(0,1,1) : 147.7646
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : 157.6537
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 147.4938
ARIMA(1,1,0) with drift : 156.2684
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 157.1647
ARIMA(2,1,0) with drift : 186.183
ARIMA(2,1,1) : 186.7743
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 858280286: log likelihood=-70.22
AIC=142.45 AICc=143.45 BIC=142.24
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 4379.362 27123.22 20044.79 0.8601614 4.6618 0.859572 -0.4409411
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 858280286: log likelihood=-70.22
AIC=142.45 AICc=143.45 BIC=142.24
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 4379.362 27123.22 20044.79 0.8601614 4.6618 0.859572 -0.4409411
Forecasts:rownames(afr_f15plus) <- "afr_f15plus"
afr_estimates <- rbind(afr_m04, afr_f04, afr_m514, afr_f514, afr_m014, afr_f014, afr_m15plus, afr_f15plus)
#SEA predictions
sea_m04 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m04")
ARIMA(0,1,0) : 129.8095
ARIMA(0,1,0) with drift : 128.9832
ARIMA(0,1,1) : 133.4638
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 133.3583
ARIMA(1,1,0) with drift : 138.4097
ARIMA(1,1,1) : 143.2038
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 143.3582
ARIMA(2,1,0) with drift : 165.0954
ARIMA(2,1,1) : 173.2038
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) with drift
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
7409.833
s.e. 2361.681
sigma^2 estimated as 40157453: log likelihood=-60.49
AIC=124.98 AICc=128.98 BIC=124.57
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -0.0466907 5355.735 4222.19 0.4100235 14.29603 0.569809 -0.2163359
Forecast method: ARIMA(0,1,0) with drift
Model Information:
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
7409.833
s.e. 2361.681
sigma^2 estimated as 40157453: log likelihood=-60.49
AIC=124.98 AICc=128.98 BIC=124.57
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -0.0466907 5355.735 4222.19 0.4100235 14.29603 0.569809 -0.2163359
Forecasts:rownames(sea_m04) <- "sea_m04"
sea_f04 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f04")
ARIMA(0,1,0) : 126.2488
ARIMA(0,1,0) with drift : 125.9369
ARIMA(0,1,1) : 130.0851
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 129.9836
ARIMA(1,1,0) with drift : 135.4326
ARIMA(1,1,1) : 139.8509
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 139.9764
ARIMA(2,1,0) with drift : 162.569
ARIMA(2,1,1) : 169.8484
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) with drift
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
5355.00
s.e. 1832.19
sigma^2 estimated as 24169343: log likelihood=-58.97
AIC=121.94 AICc=125.94 BIC=121.52
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.1219997 4154.975 3249.836 0.6199827 13.78026 0.6068789 -0.1951322
Forecast method: ARIMA(0,1,0) with drift
Model Information:
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
5355.00
s.e. 1832.19
sigma^2 estimated as 24169343: log likelihood=-58.97
AIC=121.94 AICc=125.94 BIC=121.52
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.1219997 4154.975 3249.836 0.6199827 13.78026 0.6068789 -0.1951322
Forecasts:rownames(sea_f04) <- "sea_f04"
sea_m514 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m514")
ARIMA(0,1,0) : 138.7575
ARIMA(0,1,0) with drift : 141.1449
ARIMA(0,1,1) : 143.4767
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : 153.3661
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 143.3658
ARIMA(1,1,0) with drift : 151.0966
ARIMA(1,1,1) : 153.2438
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 183.3643
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : 180.866
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 392626539: log likelihood=-67.88
AIC=137.76 AICc=138.76 BIC=137.55
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 10092.84 18344.95 10092.84 16.02839 16.02839 0.8572872 -0.2886571
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 392626539: log likelihood=-67.88
AIC=137.76 AICc=138.76 BIC=137.55
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 10092.84 18344.95 10092.84 16.02839 16.02839 0.8572872 -0.2886571
Forecasts:rownames(sea_m514) <- "sea_m514"
sea_f514 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f514")
ARIMA(0,1,0) : 139.9886
ARIMA(0,1,0) with drift : 142.6269
ARIMA(0,1,1) : 144.7946
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : 154.2335
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 144.6597
ARIMA(1,1,0) with drift : 152.6186
ARIMA(1,1,1) : 154.6524
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 184.2124
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 154.649
ARIMA(2,1,0) with drift : 182.54
ARIMA(2,1,1) : 184.4943
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 482053005: log likelihood=-68.49
AIC=138.99 AICc=139.99 BIC=138.78
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 10736.71 20327.03 11456.99 15.81939 16.8546 0.8572705 -0.2512842
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 482053005: log likelihood=-68.49
AIC=138.99 AICc=139.99 BIC=138.78
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 10736.71 20327.03 11456.99 15.81939 16.8546 0.8572705 -0.2512842
Forecasts:rownames(sea_f514) <- "sea_f514"
sea_m014 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m014")
ARIMA(0,1,0) : 143.1301
ARIMA(0,1,0) with drift : 144.5331
ARIMA(0,1,1) : 147.643
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : 157.5019
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 147.499
ARIMA(1,1,0) with drift : 154.2417
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 186.9908
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 157.4961
ARIMA(2,1,0) with drift : 183.4944
ARIMA(2,1,1) : 187.4946
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 813725760: log likelihood=-70.07
AIC=142.13 AICc=143.13 BIC=141.92
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 16421.31 26409.83 16421.31 17.32705 17.32705 0.8572857 -0.387646
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 813725760: log likelihood=-70.07
AIC=142.13 AICc=143.13 BIC=141.92
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 16421.31 26409.83 16421.31 17.32705 17.32705 0.8572857 -0.387646
Forecasts:rownames(sea_m014) <- "sea_m014"
sea_f014 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f014")
ARIMA(0,1,0) : 143.0538
ARIMA(0,1,0) with drift : 145.0229
ARIMA(0,1,1) : 147.7415
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : 157.2544
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 147.5565
ARIMA(1,1,0) with drift : 154.9293
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 187.2272
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 157.5482
ARIMA(2,1,0) with drift : 184.6836
ARIMA(2,1,1) : 187.3948
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 803450152: log likelihood=-70.03
AIC=142.05 AICc=143.05 BIC=141.85
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 15302.9 26242.55 15302.9 16.59731 16.59731 0.8572895 -0.3224274
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 803450152: log likelihood=-70.03
AIC=142.05 AICc=143.05 BIC=141.85
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 15302.9 26242.55 15302.9 16.59731 16.59731 0.8572895 -0.3224274
Forecasts:rownames(sea_f014) <- "sea_f014"
sea_m15plus <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m15plus")
ARIMA(0,1,0) : 176.6742
ARIMA(0,1,0) with drift : 179.3839
ARIMA(0,1,1) : 181.6143
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : 191.2782
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 181.5536
ARIMA(1,1,0) with drift : 189.2337
ARIMA(1,1,1) : 191.3233
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 221.1653
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 191.4064
ARIMA(2,1,0) with drift : 219.2186
ARIMA(2,1,1) : 221.2795
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 2.18e+11: log likelihood=-86.84
AIC=175.67 AICc=176.67 BIC=175.47
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 225490.8 432281.4 237748.2 14.63527 15.40864 0.8573248 -0.3031529
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 2.18e+11: log likelihood=-86.84
AIC=175.67 AICc=176.67 BIC=175.47
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 225490.8 432281.4 237748.2 14.63527 15.40864 0.8573248 -0.3031529
Forecasts:rownames(sea_m15plus) <- "sea_m15plus"
sea_f15plus <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f15plus")
ARIMA(0,1,0) : 168.8186
ARIMA(0,1,0) with drift : 170.5037
ARIMA(0,1,1) : 173.6346
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 173.459
ARIMA(1,1,0) with drift : 180.1729
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 183.2908
ARIMA(2,1,0) with drift : 210.0185
ARIMA(2,1,1) : 213.0905
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 5.887e+10: log likelihood=-82.91
AIC=167.82 AICc=168.82 BIC=167.61
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 135527 224627.7 135527 15.54276 15.54276 0.8573545 -0.3868371
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 5.887e+10: log likelihood=-82.91
AIC=167.82 AICc=168.82 BIC=167.61
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 135527 224627.7 135527 15.54276 15.54276 0.8573545 -0.3868371
Forecasts:rownames(sea_f15plus) <- "sea_f15plus"
sea_estimates <- rbind(sea_m04, sea_f04, sea_m514, sea_f514, sea_m014, sea_f014, sea_m15plus, sea_f15plus)
#WPR estimates
wpr_m04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m04")
ARIMA(0,1,0) : 117.987
ARIMA(0,1,0) with drift : 120.6596
ARIMA(0,1,1) : 122.6039
ARIMA(0,1,1) with drift : 130.6566
ARIMA(0,1,2) : 132.1259
ARIMA(0,1,2) with drift : 160.3447
ARIMA(1,1,0) : 122.4011
ARIMA(1,1,0) with drift : 130.6558
ARIMA(1,1,1) : 132.1779
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 161.9353
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 131.972
ARIMA(2,1,0) with drift : 160.5433
ARIMA(2,1,1) : 161.9623
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 12318696: log likelihood=-57.49
AIC=116.99 AICc=117.99 BIC=116.78
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1705.945 3249.443 2805.374 20.95097 30.38595 0.8571698 -0.1090836
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 12318696: log likelihood=-57.49
AIC=116.99 AICc=117.99 BIC=116.78
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1705.945 3249.443 2805.374 20.95097 30.38595 0.8571698 -0.1090836
Forecasts:rownames(wpr_m04) <- "wpr_m04"
wpr_f04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f04")
ARIMA(0,1,0) : 115.7528
ARIMA(0,1,0) with drift : 118.5332
ARIMA(0,1,1) : 120.4465
ARIMA(0,1,1) with drift : 128.5172
ARIMA(0,1,2) : 130.0029
ARIMA(0,1,2) with drift : 158.2683
ARIMA(1,1,0) : 120.2961
ARIMA(1,1,0) with drift : 128.5132
ARIMA(1,1,1) : 130.083
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 159.8347
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 129.8787
ARIMA(2,1,0) with drift : 158.4132
ARIMA(2,1,1) : 159.8769
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 8488853: log likelihood=-56.38
AIC=114.75 AICc=115.75 BIC=114.54
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1388.774 2697.436 2351.631 21.47588 31.76637 0.8571647 -0.1220096
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 8488853: log likelihood=-56.38
AIC=114.75 AICc=115.75 BIC=114.54
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1388.774 2697.436 2351.631 21.47588 31.76637 0.8571647 -0.1220096
Forecasts:rownames(wpr_f04) <- "wpr_f04"
wpr_m514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m514")
ARIMA(0,1,0) : 121.504
ARIMA(0,1,0) with drift : 123.8299
ARIMA(0,1,1) : 126.3435
ARIMA(0,1,1) with drift : 133.7516
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 126.0875
ARIMA(1,1,0) with drift : 133.6501
ARIMA(1,1,1) : 135.4448
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 22137609: log likelihood=-59.25
AIC=120.5 AICc=121.5 BIC=120.3
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2418.811 4356.041 3571.382 17.62742 23.71631 0.8572347 -0.263211
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 22137609: log likelihood=-59.25
AIC=120.5 AICc=121.5 BIC=120.3
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2418.811 4356.041 3571.382 17.62742 23.71631 0.8572347 -0.263211
Forecasts:rownames(wpr_m514) <- "wpr_m514"
wpr_f514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f514")
ARIMA(0,1,0) : 119.8234
ARIMA(0,1,0) with drift : 122.1114
ARIMA(0,1,1) : 124.7307
ARIMA(0,1,1) with drift : 131.9174
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 124.5719
ARIMA(1,1,0) with drift : 131.6532
ARIMA(1,1,1) : 133.8761
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 16729524: log likelihood=-58.41
AIC=118.82 AICc=119.82 BIC=118.62
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2114.529 3786.765 3049.1 17.08469 22.6313 0.8572513 -0.3350534
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 16729524: log likelihood=-58.41
AIC=118.82 AICc=119.82 BIC=118.62
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2114.529 3786.765 3049.1 17.08469 22.6313 0.8572513 -0.3350534
Forecasts:rownames(wpr_f514) <- "wpr_f514"
wpr_m014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m014")
ARIMA(0,1,0) : 127.329
ARIMA(0,1,0) with drift : 129.3522
ARIMA(0,1,1) : 132.0091
ARIMA(0,1,1) with drift : 139.3173
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 131.6732
ARIMA(1,1,0) with drift : 139.2927
ARIMA(1,1,1) : 141.1479
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 140.1957
ARIMA(2,1,0) with drift : 168.6569
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 58446545: log likelihood=-62.16
AIC=126.33 AICc=127.33 BIC=126.12
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 4098.811 7077.926 6008.525 18.28722 24.50654 0.8572381 -0.1974907
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 58446545: log likelihood=-62.16
AIC=126.33 AICc=127.33 BIC=126.12
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 4098.811 7077.926 6008.525 18.28722 24.50654 0.8572381 -0.1974907
Forecasts:rownames(wpr_m014) <- "wpr_m014"
wpr_f014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f014")
ARIMA(0,1,0) : 125.4245
ARIMA(0,1,0) with drift : 127.5424
ARIMA(0,1,1) : 130.1948
ARIMA(0,1,1) with drift : 137.4582
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 129.9333
ARIMA(1,1,0) with drift : 137.3915
ARIMA(1,1,1) : 139.3634
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 138.0696
ARIMA(2,1,0) with drift : 166.547
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 42550393: log likelihood=-61.21
AIC=124.42 AICc=125.42 BIC=124.22
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 3453.738 6039.186 5152.595 18.00039 24.46396 0.8572418 -0.2381519
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 42550393: log likelihood=-61.21
AIC=124.42 AICc=125.42 BIC=124.22
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 3453.738 6039.186 5152.595 18.00039 24.46396 0.8572418 -0.2381519
Forecasts:rownames(wpr_f014) <- "wpr_f014"
wpr_m15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m15plus")
ARIMA(0,1,0) : 151.1578
ARIMA(0,1,0) with drift : 155.1099
ARIMA(0,1,1) : 156.1526
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 156.1532
ARIMA(1,1,0) with drift : 163.9579
ARIMA(1,1,1) : 166.1515
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 166.0983
ARIMA(2,1,0) with drift : 193.0796
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 3.101e+09: log likelihood=-74.08
AIC=150.16 AICc=151.16 BIC=149.95
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 19216.58 51559.25 39981.73 2.127952 4.821061 0.8594709 -0.2359366
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 3.101e+09: log likelihood=-74.08
AIC=150.16 AICc=151.16 BIC=149.95
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 19216.58 51559.25 39981.73 2.127952 4.821061 0.8594709 -0.2359366
Forecasts:rownames(wpr_m15plus) <- "wpr_m15plus"
wpr_f15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f15plus")
ARIMA(0,1,0) : 143.6556
ARIMA(0,1,0) with drift : 146.9362
ARIMA(0,1,1) : 148.6553
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 148.6553
ARIMA(1,1,0) with drift : 154.5376
ARIMA(1,1,1) : 158.6553
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 158.6417
ARIMA(2,1,0) with drift : 182.9765
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 888234487: log likelihood=-70.33
AIC=142.66 AICc=143.66 BIC=142.45
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 12800.77 27592.46 20122.77 3.026658 5.073829 0.8592559 -0.3366906
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 888234487: log likelihood=-70.33
AIC=142.66 AICc=143.66 BIC=142.45
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 12800.77 27592.46 20122.77 3.026658 5.073829 0.8592559 -0.3366906
Forecasts:rownames(wpr_f15plus) <- "wpr_f15plus"
wpr_estimates <- rbind(wpr_m04, wpr_f04, wpr_m514, wpr_f514, wpr_m014, wpr_f014, wpr_m15plus, wpr_f15plus)
#EUR Estimates
eur_m04 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m04")
ARIMA(0,1,0) : 78.75381
ARIMA(0,1,0) with drift : 82.12951
ARIMA(0,1,1) : 83.64837
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : 93.2627
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 83.6904
ARIMA(1,1,0) with drift : 92.06954
ARIMA(1,1,1) : 93.5146
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 123.2595
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 17815: log likelihood=-37.88
AIC=77.75 AICc=78.75 BIC=77.55
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -55.44929 123.5715 83.40786 -3.641441 5.183049 0.8598748 -0.1116328
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 17815: log likelihood=-37.88
AIC=77.75 AICc=78.75 BIC=77.55
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -55.44929 123.5715 83.40786 -3.641441 5.183049 0.8598748 -0.1116328
Forecasts:rownames(eur_m04) <- "eur_m04"
eur_f04 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f04")
ARIMA(0,1,0) : 76.68985
ARIMA(0,1,0) with drift : 80.33647
ARIMA(0,1,1) : 81.58578
ARIMA(0,1,1) with drift : 90.27588
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 81.57517
ARIMA(1,1,0) with drift : 90.28316
ARIMA(1,1,1) : 91.57496
ARIMA(1,1,1) with drift : 120.2759
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : 120.15
ARIMA(2,1,1) : 121.4982
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 12630: log likelihood=-36.84
AIC=75.69 AICc=76.69 BIC=75.48
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -43.06114 104.045 79.22457 -3.311926 5.619945 0.8595794 0.004291823
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 12630: log likelihood=-36.84
AIC=75.69 AICc=76.69 BIC=75.48
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -43.06114 104.045 79.22457 -3.311926 5.619945 0.8595794 0.004291823
Forecasts:rownames(eur_f04) <- "eur_f04"
eur_m514 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m514")
ARIMA(0,1,0) : 85.509
ARIMA(0,1,0) with drift : 86.43678
ARIMA(0,1,1) : Inf
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 90.11173
ARIMA(1,1,0) with drift : 95.54597
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 100.0673
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 54922: log likelihood=-41.25
AIC=84.51 AICc=85.51 BIC=84.3
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -140.4223 216.9705 141.5777 -4.173495 4.202066 0.8606548 -0.4670638
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 54922: log likelihood=-41.25
AIC=84.51 AICc=85.51 BIC=84.3
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -140.4223 216.9705 141.5777 -4.173495 4.202066 0.8606548 -0.4670638
Forecasts:rownames(eur_m514) <- "eur_m514"
eur_f514 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f514")
ARIMA(0,1,0) : 84.80427
ARIMA(0,1,0) with drift : 86.44661
ARIMA(0,1,1) : 89.6705
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 89.57782
ARIMA(1,1,0) with drift : 94.21642
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 99.14526
ARIMA(2,1,0) with drift : 121.6276
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 48836: log likelihood=-40.9
AIC=83.8 AICc=84.8 BIC=83.6
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -123.4541 204.5953 147.1173 -3.853962 4.643237 0.860335 -0.5094926
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 48836: log likelihood=-40.9
AIC=83.8 AICc=84.8 BIC=83.6
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -123.4541 204.5953 147.1173 -3.853962 4.643237 0.860335 -0.5094926
Forecasts:rownames(eur_f514) <- "eur_f514"
eur_m014 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m014")
ARIMA(0,1,0) : 87.90563
ARIMA(0,1,0) with drift : 87.17066
ARIMA(0,1,1) : Inf
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 91.3642
ARIMA(1,1,0) with drift : 96.87938
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 101.202
ARIMA(2,1,0) with drift : 116.0782
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) with drift
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
-224.5000
s.e. 72.4391
sigma^2 estimated as 37788: log likelihood=-39.59
AIC=83.17 AICc=87.17 BIC=82.75
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.8747853 164.2911 147.3034 0.003111067 2.892103 0.6561397 -0.1918559
Forecast method: ARIMA(0,1,0) with drift
Model Information:
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
-224.5000
s.e. 72.4391
sigma^2 estimated as 37788: log likelihood=-39.59
AIC=83.17 AICc=87.17 BIC=82.75
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.8747853 164.2911 147.3034 0.003111067 2.892103 0.6561397 -0.1918559
Forecasts:rownames(eur_m014) <- "eur_m014"
eur_f014 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f014")
ARIMA(0,1,0) : 87.89843
ARIMA(0,1,0) with drift : 89.26901
ARIMA(0,1,1) : 92.88049
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : 102.6458
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 92.86608
ARIMA(1,1,0) with drift : 97.28552
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 132.2977
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 102.1516
ARIMA(2,1,0) with drift : 126.8821
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 81791: log likelihood=-42.45
AIC=86.9 AICc=87.9 BIC=86.69
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -164.3724 264.7762 197.3419 -3.582997 4.247259 0.8605023 -0.5893928
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 81791: log likelihood=-42.45
AIC=86.9 AICc=87.9 BIC=86.69
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -164.3724 264.7762 197.3419 -3.582997 4.247259 0.8605023 -0.5893928
Forecasts:rownames(eur_f014) <- "eur_f014"
eur_m15plus <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m15plus")
ARIMA(0,1,0) : 125.9029
ARIMA(0,1,0) with drift : 120.3483
ARIMA(0,1,1) : Inf
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : Inf
ARIMA(1,1,0) with drift : 128.3634
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 129.0743
ARIMA(2,1,0) with drift : 155.9324
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) with drift
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
-6176.333
s.e. 1150.038
sigma^2 estimated as 9528860: log likelihood=-56.17
AIC=116.35 AICc=120.35 BIC=115.93
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 25.60432 2608.894 2185.795 -0.08003539 1.376032 0.3538985 0.4138192
Forecast method: ARIMA(0,1,0) with drift
Model Information:
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
-6176.333
s.e. 1150.038
sigma^2 estimated as 9528860: log likelihood=-56.17
AIC=116.35 AICc=120.35 BIC=115.93
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 25.60432 2608.894 2185.795 -0.08003539 1.376032 0.3538985 0.4138192
Forecasts:rownames(eur_m15plus) <- "eur_m15plus"
eur_f15plus <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f15plus")
ARIMA(0,1,0) : 116.7389
ARIMA(0,1,0) with drift : 104.0178
ARIMA(0,1,1) : Inf
ARIMA(0,1,1) with drift : 113.7773
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : Inf
ARIMA(1,1,0) with drift : 113.975
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 148.6417
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : 143.0256
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) with drift
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
-3079.5000
s.e. 294.9107
sigma^2 estimated as 627839: log likelihood=-48.01
AIC=100.02 AICc=104.02 BIC=99.6
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 12.97978 669.6689 474.9798 -0.01880938 0.588106 0.1542393 0.0977167
Forecast method: ARIMA(0,1,0) with drift
Model Information:
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
-3079.5000
s.e. 294.9107
sigma^2 estimated as 627839: log likelihood=-48.01
AIC=100.02 AICc=104.02 BIC=99.6
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 12.97978 669.6689 474.9798 -0.01880938 0.588106 0.1542393 0.0977167
Forecasts:rownames(eur_f15plus) <- "eur_f15plus"
eur_estimates <- rbind(eur_m04, eur_f04, eur_m514, eur_f514, eur_m014, eur_f014, eur_m15plus, eur_f15plus)
#AMR Estimates
emr_m04 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m04")
ARIMA(0,1,0) : 110.5715
ARIMA(0,1,0) with drift : 106.0002
ARIMA(0,1,1) : 111.9022
ARIMA(0,1,1) with drift : 115.5503
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 107.6886
ARIMA(1,1,0) with drift : 115.508
ARIMA(1,1,1) : 117.6708
ARIMA(1,1,1) with drift : 145.4466
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 117.6505
ARIMA(2,1,0) with drift : 145.242
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) with drift
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
1689.1667
s.e. 347.8854
sigma^2 estimated as 871360: log likelihood=-49
AIC=102 AICc=106 BIC=101.58
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.532833 788.9232 524.0566 0.6179599 3.986697 0.3102457 0.2009148
Forecast method: ARIMA(0,1,0) with drift
Model Information:
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
1689.1667
s.e. 347.8854
sigma^2 estimated as 871360: log likelihood=-49
AIC=102 AICc=106 BIC=101.58
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.532833 788.9232 524.0566 0.6179599 3.986697 0.3102457 0.2009148
Forecasts:rownames(emr_m04) <- "emr_m04"
emr_f04 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f04")
ARIMA(0,1,0) : 107.6113
ARIMA(0,1,0) with drift : 103.7642
ARIMA(0,1,1) : 110.6912
ARIMA(0,1,1) with drift : 113.6269
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 107.7608
ARIMA(1,1,0) with drift : 113.5916
ARIMA(1,1,1) : 117.0607
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 116.1464
ARIMA(2,1,0) with drift : 143.3858
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) with drift
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
1298.1667
s.e. 288.7444
sigma^2 estimated as 600277: log likelihood=-47.88
AIC=99.76 AICc=103.76 BIC=99.35
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.4144046 654.805 447.462 0.7599193 4.915919 0.3446877 -0.1203015
Forecast method: ARIMA(0,1,0) with drift
Model Information:
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
1298.1667
s.e. 288.7444
sigma^2 estimated as 600277: log likelihood=-47.88
AIC=99.76 AICc=103.76 BIC=99.35
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.4144046 654.805 447.462 0.7599193 4.915919 0.3446877 -0.1203015
Forecasts:rownames(emr_f04) <- "emr_f04"
emr_m514 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m514")
ARIMA(0,1,0) : 106.2175
ARIMA(0,1,0) with drift : 106.5495
ARIMA(0,1,1) : Inf
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 108.4115
ARIMA(1,1,0) with drift : 116.5418
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 117.5946
ARIMA(2,1,0) with drift : 144.3844
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 1732475: log likelihood=-51.61
AIC=105.22 AICc=106.22 BIC=105.01
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 831.4019 1218.597 893.9733 5.034561 5.531396 0.8589015 0.2435649
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 1732475: log likelihood=-51.61
AIC=105.22 AICc=106.22 BIC=105.01
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 831.4019 1218.597 893.9733 5.034561 5.531396 0.8589015 0.2435649
Forecasts:rownames(emr_m514) <- "emr_m514"
emr_f514 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f514")
ARIMA(0,1,0) : 106.7125
ARIMA(0,1,0) with drift : 109.5098
ARIMA(0,1,1) : Inf
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 110.6453
ARIMA(1,1,0) with drift : 119.4974
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 119.929
ARIMA(2,1,0) with drift : 148.2944
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 1881467: log likelihood=-51.86
AIC=105.71 AICc=106.71 BIC=105.5
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 654.1529 1269.916 998.7243 3.163926 5.159425 0.8592409 0.1308076
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 1881467: log likelihood=-51.86
AIC=105.71 AICc=106.71 BIC=105.5
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 654.1529 1269.916 998.7243 3.163926 5.159425 0.8592409 0.1308076
Forecasts:rownames(emr_f514) <- "emr_f514"
emr_m014 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m014")
ARIMA(0,1,0) : 117.0617
ARIMA(0,1,0) with drift : 115.5705
ARIMA(0,1,1) : 118.5877
ARIMA(0,1,1) with drift : 125.0624
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 116.8657
ARIMA(1,1,0) with drift : 125.2341
ARIMA(1,1,1) : 126.7096
ARIMA(1,1,1) with drift : 155.0521
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 126.5296
ARIMA(2,1,0) with drift : 154.4364
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) with drift
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
2641.8333
s.e. 772.3193
sigma^2 estimated as 4294599: log likelihood=-53.79
AIC=111.57 AICc=115.57 BIC=111.15
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2.240166 1751.448 1276.383 0.1088444 4.518639 0.4637739 0.1764573
Forecast method: ARIMA(0,1,0) with drift
Model Information:
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
2641.8333
s.e. 772.3193
sigma^2 estimated as 4294599: log likelihood=-53.79
AIC=111.57 AICc=115.57 BIC=111.15
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2.240166 1751.448 1276.383 0.1088444 4.518639 0.4637739 0.1764573
Forecasts:rownames(emr_m014) <- "emr_m014"
emr_f014 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f014")
ARIMA(0,1,0) : 114.8804
ARIMA(0,1,0) with drift : 114.8509
ARIMA(0,1,1) : 116.6524
ARIMA(0,1,1) with drift : 124.3216
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 115.7161
ARIMA(1,1,0) with drift : 124.5359
ARIMA(1,1,1) : 125.4571
ARIMA(1,1,1) with drift : 154.3157
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 125.0684
ARIMA(2,1,0) with drift : 153.5805
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) with drift
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
2041.0000
s.e. 727.3703
sigma^2 estimated as 3809285: log likelihood=-53.43
AIC=110.85 AICc=114.85 BIC=110.43
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2.761856 1649.52 1225.619 0.05313471 4.220174 0.5291965 0.162671
Forecast method: ARIMA(0,1,0) with drift
Model Information:
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
2041.0000
s.e. 727.3703
sigma^2 estimated as 3809285: log likelihood=-53.43
AIC=110.85 AICc=114.85 BIC=110.43
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2.761856 1649.52 1225.619 0.05313471 4.220174 0.5291965 0.162671
Forecasts:rownames(emr_f014) <- "emr_f014"
emr_m15plus <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m15plus")
ARIMA(0,1,0) : 133.2224
ARIMA(0,1,0) with drift : 136.4537
ARIMA(0,1,1) : 138.0252
ARIMA(0,1,1) with drift : 146.4274
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : 176.2163
ARIMA(1,1,0) : 137.8248
ARIMA(1,1,0) with drift : 146.4201
ARIMA(1,1,1) : 147.6019
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 147.0673
ARIMA(2,1,0) with drift : 176.3108
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 156081396: log likelihood=-65.11
AIC=132.22 AICc=133.22 BIC=132.01
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 5437.489 11566.51 8570.917 2.486889 3.87156 0.8598432 -0.120371
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 156081396: log likelihood=-65.11
AIC=132.22 AICc=133.22 BIC=132.01
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 5437.489 11566.51 8570.917 2.486889 3.87156 0.8598432 -0.120371
Forecasts:rownames(emr_m15plus) <- "emr_m15plus"
emr_f15plus <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f15plus")
ARIMA(0,1,0) : 131.2118
ARIMA(0,1,0) with drift : 135.5512
ARIMA(0,1,1) : 136.2115
ARIMA(0,1,1) with drift : 145.4442
ARIMA(0,1,2) : 145.3951
ARIMA(0,1,2) with drift : 175.3264
ARIMA(1,1,0) : 136.2112
ARIMA(1,1,0) with drift : 145.4197
ARIMA(1,1,1) : 146.1754
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 145.6273
ARIMA(2,1,0) with drift : 175.2787
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 111641770: log likelihood=-64.11
AIC=130.21 AICc=131.21 BIC=130
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2949.988 9782.277 7055.131 1.426539 3.469009 0.8602944 -0.1560189
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 111641770: log likelihood=-64.11
AIC=130.21 AICc=131.21 BIC=130
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2949.988 9782.277 7055.131 1.426539 3.469009 0.8602944 -0.1560189
Forecasts:rownames(emr_f15plus) <- "emr_f15plus"
emr_estimates <- rbind(emr_m04, emr_f04, emr_m514, emr_f514, emr_m014, emr_f014, emr_m15plus, emr_f15plus)
#WPR Estimates
wpr_m04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m04")
ARIMA(0,1,0) : 117.987
ARIMA(0,1,0) with drift : 120.6596
ARIMA(0,1,1) : 122.6039
ARIMA(0,1,1) with drift : 130.6566
ARIMA(0,1,2) : 132.1259
ARIMA(0,1,2) with drift : 160.3447
ARIMA(1,1,0) : 122.4011
ARIMA(1,1,0) with drift : 130.6558
ARIMA(1,1,1) : 132.1779
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 161.9353
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 131.972
ARIMA(2,1,0) with drift : 160.5433
ARIMA(2,1,1) : 161.9623
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 12318696: log likelihood=-57.49
AIC=116.99 AICc=117.99 BIC=116.78
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1705.945 3249.443 2805.374 20.95097 30.38595 0.8571698 -0.1090836
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 12318696: log likelihood=-57.49
AIC=116.99 AICc=117.99 BIC=116.78
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1705.945 3249.443 2805.374 20.95097 30.38595 0.8571698 -0.1090836
Forecasts:rownames(wpr_m04) <- "wpr_m04"
wpr_f04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f04")
ARIMA(0,1,0) : 115.7528
ARIMA(0,1,0) with drift : 118.5332
ARIMA(0,1,1) : 120.4465
ARIMA(0,1,1) with drift : 128.5172
ARIMA(0,1,2) : 130.0029
ARIMA(0,1,2) with drift : 158.2683
ARIMA(1,1,0) : 120.2961
ARIMA(1,1,0) with drift : 128.5132
ARIMA(1,1,1) : 130.083
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 159.8347
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 129.8787
ARIMA(2,1,0) with drift : 158.4132
ARIMA(2,1,1) : 159.8769
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 8488853: log likelihood=-56.38
AIC=114.75 AICc=115.75 BIC=114.54
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1388.774 2697.436 2351.631 21.47588 31.76637 0.8571647 -0.1220096
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 8488853: log likelihood=-56.38
AIC=114.75 AICc=115.75 BIC=114.54
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1388.774 2697.436 2351.631 21.47588 31.76637 0.8571647 -0.1220096
Forecasts:rownames(wpr_f04) <- "wpr_f04"
wpr_m514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m514")
ARIMA(0,1,0) : 121.504
ARIMA(0,1,0) with drift : 123.8299
ARIMA(0,1,1) : 126.3435
ARIMA(0,1,1) with drift : 133.7516
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 126.0875
ARIMA(1,1,0) with drift : 133.6501
ARIMA(1,1,1) : 135.4448
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 22137609: log likelihood=-59.25
AIC=120.5 AICc=121.5 BIC=120.3
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2418.811 4356.041 3571.382 17.62742 23.71631 0.8572347 -0.263211
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 22137609: log likelihood=-59.25
AIC=120.5 AICc=121.5 BIC=120.3
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2418.811 4356.041 3571.382 17.62742 23.71631 0.8572347 -0.263211
Forecasts:rownames(wpr_m514) <- "wpr_m514"
wpr_f514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f514")
ARIMA(0,1,0) : 119.8234
ARIMA(0,1,0) with drift : 122.1114
ARIMA(0,1,1) : 124.7307
ARIMA(0,1,1) with drift : 131.9174
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 124.5719
ARIMA(1,1,0) with drift : 131.6532
ARIMA(1,1,1) : 133.8761
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 16729524: log likelihood=-58.41
AIC=118.82 AICc=119.82 BIC=118.62
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2114.529 3786.765 3049.1 17.08469 22.6313 0.8572513 -0.3350534
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 16729524: log likelihood=-58.41
AIC=118.82 AICc=119.82 BIC=118.62
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2114.529 3786.765 3049.1 17.08469 22.6313 0.8572513 -0.3350534
Forecasts:rownames(wpr_f514) <- "wpr_f514"
wpr_m014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m014")
ARIMA(0,1,0) : 127.329
ARIMA(0,1,0) with drift : 129.3522
ARIMA(0,1,1) : 132.0091
ARIMA(0,1,1) with drift : 139.3173
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 131.6732
ARIMA(1,1,0) with drift : 139.2927
ARIMA(1,1,1) : 141.1479
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 140.1957
ARIMA(2,1,0) with drift : 168.6569
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 58446545: log likelihood=-62.16
AIC=126.33 AICc=127.33 BIC=126.12
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 4098.811 7077.926 6008.525 18.28722 24.50654 0.8572381 -0.1974907
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 58446545: log likelihood=-62.16
AIC=126.33 AICc=127.33 BIC=126.12
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 4098.811 7077.926 6008.525 18.28722 24.50654 0.8572381 -0.1974907
Forecasts:rownames(wpr_m014) <- "wpr_m014"
wpr_f014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f014")
ARIMA(0,1,0) : 125.4245
ARIMA(0,1,0) with drift : 127.5424
ARIMA(0,1,1) : 130.1948
ARIMA(0,1,1) with drift : 137.4582
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 129.9333
ARIMA(1,1,0) with drift : 137.3915
ARIMA(1,1,1) : 139.3634
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 138.0696
ARIMA(2,1,0) with drift : 166.547
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 42550393: log likelihood=-61.21
AIC=124.42 AICc=125.42 BIC=124.22
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 3453.738 6039.186 5152.595 18.00039 24.46396 0.8572418 -0.2381519
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 42550393: log likelihood=-61.21
AIC=124.42 AICc=125.42 BIC=124.22
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 3453.738 6039.186 5152.595 18.00039 24.46396 0.8572418 -0.2381519
Forecasts:rownames(wpr_f014) <- "wpr_f014"
wpr_m15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m15plus")
ARIMA(0,1,0) : 151.1578
ARIMA(0,1,0) with drift : 155.1099
ARIMA(0,1,1) : 156.1526
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 156.1532
ARIMA(1,1,0) with drift : 163.9579
ARIMA(1,1,1) : 166.1515
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 166.0983
ARIMA(2,1,0) with drift : 193.0796
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 3.101e+09: log likelihood=-74.08
AIC=150.16 AICc=151.16 BIC=149.95
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 19216.58 51559.25 39981.73 2.127952 4.821061 0.8594709 -0.2359366
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 3.101e+09: log likelihood=-74.08
AIC=150.16 AICc=151.16 BIC=149.95
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 19216.58 51559.25 39981.73 2.127952 4.821061 0.8594709 -0.2359366
Forecasts:rownames(wpr_m15plus) <- "wpr_m15plus"
wpr_f15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f15plus")
ARIMA(0,1,0) : 143.6556
ARIMA(0,1,0) with drift : 146.9362
ARIMA(0,1,1) : 148.6553
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 148.6553
ARIMA(1,1,0) with drift : 154.5376
ARIMA(1,1,1) : 158.6553
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 158.6417
ARIMA(2,1,0) with drift : 182.9765
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 888234487: log likelihood=-70.33
AIC=142.66 AICc=143.66 BIC=142.45
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 12800.77 27592.46 20122.77 3.026658 5.073829 0.8592559 -0.3366906
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 888234487: log likelihood=-70.33
AIC=142.66 AICc=143.66 BIC=142.45
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 12800.77 27592.46 20122.77 3.026658 5.073829 0.8592559 -0.3366906
Forecasts:rownames(wpr_f15plus) <- "wpr_f15plus"
wpr_estimates <- rbind(wpr_m04, wpr_f04, wpr_m514, wpr_f514, wpr_m014, wpr_f014, wpr_m15plus, wpr_f15plus)
#AMR Estimates
amr_m04 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m04")
ARIMA(0,1,0) : 81.20312
ARIMA(0,1,0) with drift : 85.62773
ARIMA(0,1,1) : Inf
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 83.36317
ARIMA(1,1,0) with drift : 93.10285
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 91.99538
ARIMA(2,1,0) with drift : 121.9132
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 26796: log likelihood=-39.1
AIC=80.2 AICc=81.2 BIC=79.99
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -42.09157 151.5522 120.1941 -2.150665 5.588892 0.8595529 -0.4510363
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 26796: log likelihood=-39.1
AIC=80.2 AICc=81.2 BIC=79.99
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -42.09157 151.5522 120.1941 -2.150665 5.588892 0.8595529 -0.4510363
Forecasts:rownames(amr_m04) <- "amr_m04"
amr_f04 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f04")
ARIMA(0,1,0) : 79.78439
ARIMA(0,1,0) with drift : 84.32867
ARIMA(0,1,1) : 81.41097
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 80.0876
ARIMA(1,1,0) with drift : 89.04987
ARIMA(1,1,1) : 89.05019
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 88.27209
ARIMA(2,1,0) with drift : 116.0278
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 21153: log likelihood=-38.39
AIC=78.78 AICc=79.78 BIC=78.58
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -33.42686 134.653 118.8589 -2.025789 6.38553 0.8592207 -0.7441053
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 21153: log likelihood=-38.39
AIC=78.78 AICc=79.78 BIC=78.58
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -33.42686 134.653 118.8589 -2.025789 6.38553 0.8592207 -0.7441053
Forecasts:rownames(amr_f04) <- "amr_f04"
amr_m514 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m514")
ARIMA(0,1,0) : 80.43822
ARIMA(0,1,0) with drift : 82.89086
ARIMA(0,1,1) : 85.35448
ARIMA(0,1,1) with drift : 92.87508
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 85.25797
ARIMA(1,1,0) with drift : 92.87029
ARIMA(1,1,1) : 94.93816
ARIMA(1,1,1) with drift : 122.6887
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 94.65097
ARIMA(2,1,0) with drift : 122.7689
ARIMA(2,1,1) : 124.598
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 23590: log likelihood=-38.72
AIC=79.44 AICc=80.44 BIC=79.23
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -76.94743 142.1967 104.7669 -2.600088 3.547003 0.8610975 -0.2447804
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 23590: log likelihood=-38.72
AIC=79.44 AICc=80.44 BIC=79.23
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -76.94743 142.1967 104.7669 -2.600088 3.547003 0.8610975 -0.2447804
Forecasts:rownames(amr_m514) <- "amr_m514"
amr_f514 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f514")
ARIMA(0,1,0) : 81.77671
ARIMA(0,1,0) with drift : 85.53773
ARIMA(0,1,1) : 85.39397
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 84.22718
ARIMA(1,1,0) with drift : 92.24655
ARIMA(1,1,1) : 93.97957
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 29485: log likelihood=-39.39
AIC=80.78 AICc=81.78 BIC=80.57
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -63.094 158.9745 120.906 -2.160652 3.97228 0.8605409 -0.510281
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 29485: log likelihood=-39.39
AIC=80.78 AICc=81.78 BIC=80.57
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -63.094 158.9745 120.906 -2.160652 3.97228 0.8605409 -0.510281
Forecasts:rownames(amr_f514) <- "amr_f514"
amr_m014 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m014")
ARIMA(0,1,0) : 84.45934
ARIMA(0,1,0) with drift : 87.54733
ARIMA(0,1,1) : 89.18806
ARIMA(0,1,1) with drift : 97.48774
ARIMA(0,1,2) : 99.10081
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 89.11319
ARIMA(1,1,0) with drift : 97.52031
ARIMA(1,1,1) : 99.10554
ARIMA(1,1,1) with drift : 127.4837
ARIMA(1,1,2) : 129.0897
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : 127.3181
ARIMA(2,1,1) : 129.1039
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 46111: log likelihood=-40.73
AIC=83.46 AICc=84.46 BIC=83.25
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -95.30943 198.8054 97.83343 -1.802642 1.847534 0.8645075 -0.1921184
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 46111: log likelihood=-40.73
AIC=83.46 AICc=84.46 BIC=83.25
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -95.30943 198.8054 97.83343 -1.802642 1.847534 0.8645075 -0.1921184
Forecasts:rownames(amr_m014) <- "amr_m014"
amr_f014 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f014")
ARIMA(0,1,0) : 81.98907
ARIMA(0,1,0) with drift : 85.52465
ARIMA(0,1,1) : 86.57645
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 86.05811
ARIMA(1,1,0) with drift : 93.61714
ARIMA(1,1,1) : 95.78387
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 95.5139
ARIMA(2,1,0) with drift : 123.5324
ARIMA(2,1,1) : 125.511
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 30550: log likelihood=-39.49
AIC=80.99 AICc=81.99 BIC=80.78
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -68.933 161.8208 117.067 -1.369866 2.307828 0.8629017 -0.5168809
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 30550: log likelihood=-39.49
AIC=80.99 AICc=81.99 BIC=80.78
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -68.933 161.8208 117.067 -1.369866 2.307828 0.8629017 -0.5168809
Forecasts:rownames(amr_f014) <- "amr_f014"
amr_m15plus <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m15plus")
ARIMA(0,1,0) : 121.5033
ARIMA(0,1,0) with drift : 121.2176
ARIMA(0,1,1) : 124.9374
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : 133.9693
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 124.2796
ARIMA(1,1,0) with drift : 130.5986
ARIMA(1,1,1) : 134.1506
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 163.6115
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 134.0446
ARIMA(2,1,0) with drift : 160.3891
ARIMA(2,1,1) : 164.0426
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) with drift
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
3600.333
s.e. 1236.437
sigma^2 estimated as 11010114: log likelihood=-56.61
AIC=117.22 AICc=121.22 BIC=116.8
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 17.92894 2804.348 2433.643 -0.0223538 1.731512 0.6560275 -0.2334609
Forecast method: ARIMA(0,1,0) with drift
Model Information:
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
3600.333
s.e. 1236.437
sigma^2 estimated as 11010114: log likelihood=-56.61
AIC=117.22 AICc=121.22 BIC=116.8
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 17.92894 2804.348 2433.643 -0.0223538 1.731512 0.6560275 -0.2334609
Forecasts:rownames(amr_m15plus) <- "amr_m15plus"
amr_f15plus <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f15plus")
ARIMA(0,1,0) : 107.077
ARIMA(0,1,0) with drift : 111.465
ARIMA(0,1,1) : 111.6485
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 111.8425
ARIMA(1,1,0) with drift : 120.2414
ARIMA(1,1,1) : 121.6324
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 120.2146
ARIMA(2,1,0) with drift : 144.5938
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 2e+06: log likelihood=-52.04
AIC=106.08 AICc=107.08 BIC=105.87
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 388.1107 1309.374 1138.968 0.4951078 1.50792 0.8652579 -0.2900684
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 2e+06: log likelihood=-52.04
AIC=106.08 AICc=107.08 BIC=105.87
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 388.1107 1309.374 1138.968 0.4951078 1.50792 0.8652579 -0.2900684
Forecasts:rownames(amr_f15plus) <- "amr_f15plus"
amr_estimates <- rbind(amr_m04, amr_f04, amr_m514, amr_f514, amr_m014, amr_f014, amr_m15plus, amr_f15plus)
Combining the data for 2020 with the model estimates
##AFR difference calculations
afr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "AFR") %>% t()
afr_2020 <- as.data.frame(afr_2020[c(3:10), ])
rownames(afr_2020) <- c("afr_m04", "afr_f04", "afr_m514", "afr_f514", "afr_m014", "afr_f014", "afr_m15plus", "afr_f15plus")
colnames(afr_2020) <- "notif_2020"
est_afr_2020 <- cbind(afr_2020, afr_estimates)
colnames(est_afr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_afr_2020$notif_2020 <- as.numeric(est_afr_2020$notif_2020)
dif_afr <- mutate(est_afr_2020, "Difference" = notif_2020 - est_2020, "afr_perc" = 100*(Difference/est_2020))
##SEA difference calculations
sea_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "SEA") %>% t()
sea_2020 <- as.data.frame(sea_2020[c(3:10), ])
rownames(sea_2020) <- c("sea_m04", "sea_f04", "sea_m514", "sea_f514", "sea_m014", "sea_f014", "sea_m15plus", "sea_f15plus")
colnames(sea_2020) <- "notif_2020"
est_sea_2020 <- cbind(sea_2020, sea_estimates)
colnames(est_sea_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_sea_2020$notif_2020 <- as.numeric(est_sea_2020$notif_2020)
dif_sea <- mutate(est_sea_2020, "Difference" = notif_2020 - est_2020, "sea_perc" = 100*(Difference/est_2020))
#WPR difference calculations
wpr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "WPR") %>% t()
wpr_2020 <- as.data.frame(wpr_2020[c(3:10), ])
rownames(wpr_2020) <- c("wpr_m04", "wpr_f04", "wpr_m514", "wpr_f514", "wpr_m014", "wpr_f014", "wpr_m15plus", "wpr_f15plus")
colnames(wpr_2020) <- "wpr_2020"
est_wpr_2020 <- cbind(wpr_2020, wpr_estimates)
colnames(est_wpr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_wpr_2020$notif_2020 <- as.numeric(est_wpr_2020$notif_2020)
dif_wpr <- mutate(est_wpr_2020, "Difference" = notif_2020 - est_2020, "wpr_perc" = 100*(Difference/est_2020))
#EUR difference calculations
eur_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "EUR") %>% t()
eur_2020 <- as.data.frame(eur_2020[c(3:10), ])
rownames(eur_2020) <- c("eur_m04", "eur_f04", "eur_m514", "eur_f514", "eur_m014", "eur_f014", "eur_m15plus", "eur_f15plus")
colnames(eur_2020) <- "eur_2020"
est_eur_2020 <- cbind(eur_2020, eur_estimates)
colnames(est_eur_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_eur_2020$notif_2020 <- as.numeric(est_eur_2020$notif_2020)
dif_eur <- mutate(est_eur_2020, "Difference" = notif_2020 - est_2020, "eur_perc" = 100*(Difference/est_2020))
#AMR difference calculations
amr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "AMR") %>% t()
amr_2020 <- as.data.frame(amr_2020[c(3:10), ])
rownames(amr_2020) <- c("amr_m04", "amr_f04", "amr_m514", "amr_f514", "amr_m014", "amr_f014", "amr_m15plus", "amr_f15plus")
colnames(eur_2020) <- "amr_2020"
est_amr_2020 <- cbind(amr_2020, amr_estimates)
colnames(est_amr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_amr_2020$notif_2020 <- as.numeric(est_amr_2020$notif_2020)
dif_amr <- mutate(est_amr_2020, "Difference" = notif_2020 - est_2020, "amr_perc" = 100*(Difference/est_2020))
#EMR difference calculations
emr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "EMR") %>% t()
emr_2020 <- as.data.frame(emr_2020[c(3:10), ])
rownames(emr_2020) <- c("emr_m04", "emr_f04", "emr_m514", "emr_f514", "emr_m014", "emr_f014", "emr_m15plus", "emr_f15plus")
colnames(emr_2020) <- "emr_2020"
est_emr_2020 <- cbind(emr_2020, emr_estimates)
colnames(est_emr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_emr_2020$notif_2020 <- as.numeric(est_emr_2020$notif_2020)
dif_emr <- mutate(est_emr_2020, "Difference" = notif_2020 - est_2020, "emr_perc" = 100*(Difference/est_2020))
#Combined data frame with difference data
combined_dif <- data.frame(dif_afr[, c(7,8)], dif_amr[,c(7,8)], dif_emr[, c(7,8)], dif_eur[,c(7:8)], dif_sea[,c(7:8)], dif_wpr[,c(7:8)])
combined_dif$group <- as.vector(rownames(combined_dif))
combined_dif$group <- c("m04", "f04", "m514", "f514", "m014", "f014", "m15plus", "f15plus")
rownames(combined_dif) <- c(1:8)
combined_dif <- combined_dif[, c(13,1:12)]
colnames(combined_dif) <- c("group", "AFR_dif", "AFR_perc", "AMR_dif", "AMR_perc", "EMR_dif", "EMR_perc", "EUR_dif", "EUR_perc", "SEA_dif", "SEA_perc", "WPR_dif", "WPR_perc")
#isolating only the percentage data to be able to plot a bar plot
bar_data <- combined_dif[, c(1,3,5,7,9,11,13)]
bar_newgroup <- str_split_fixed(bar_data$group, "", 2)
bar_data2 <- cbind(bar_data, bar_newgroup)
colnames(bar_data2) <- c("group", "AFR", "AMR", "EMR", "EUR", "SEA", "WPR", "sex", "age_group")
bar_data2 <- bar_data2[, c(9,8,2:7)]
piv_dif <- pivot_longer(bar_data2, cols = c("AFR", "AMR", "EMR", "EUR", "SEA", "WPR"), names_to = "g_whoregion", values_to = "value")
library(dplyr)
library(ggplot2)
piv_three <- filter(piv_dif, age_group != "014")
##Attempt at drawing plots including 0-14
arima_dif <- ggplot(piv_dif, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_dif$age_group) + geom_bar(stat = "identity", position = "dodge")
##Attempt excluding 0-14
piv_copy <- piv_three
piv_copy$age_group <- factor(piv_copy$age_group, levels = c("04", "514", "15plus"))
arima_copy <- ggplot(piv_copy, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_copy$age_group) + geom_bar(stat = "identity", position = "dodge")
arima_copy
arima_three <- ggplot(piv_three, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_three$age_group) + geom_bar(stat = "identity", position = "dodge")
arima_three
#Attempt at the forecasting models
#Rearranging data for 0-4 males
males_04 <- select(perc_world, c(g_whoregion, year, newrel_m04))
piv_na <- pivot_wider(males_04, names_from = "year", values_from = "newrel_m04")
piv_na$`2020` <- NA
males_back <- pivot_longer(piv_na, cols = c(as.vector(colnames(piv_na[,2:9]))), names_to = "year")
males_back$year <- as.numeric(males_back$year)
males_no_pivot <- males_04
males_pivot <- pivot_wider(males_04, names_from = "g_whoregion", values_from = "newrel_m04")
select(afr_m04, "Point Forecast")
fore2020 <- function(df){
a <- select(df, "Point Forecast")
b <- "2020"
colnames(a) <- "Number"
rownames(a) <- NULL
a$Year <- b
return(a)
}
#males 04
fore_afrm04 <- fore2020(afr_m04)
fore_amrm04 <- fore2020(amr_m04)
fore_emrm04 <- fore2020(emr_m04)
fore_eurm04 <- fore2020(eur_m04)
fore_seam04 <- fore2020(sea_m04)
fore_wprm04 <- fore2020(wpr_m04)
bound_fore <- cbind(fore_afrm04, fore_amrm04$Number, fore_emrm04$Number, fore_eurm04$Number, fore_seam04$Number, fore_wprm04$Number)
colnames(bound_fore) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_fore <- bound_fore[, c(2, 1, 3:7)]
#females04
fore_afrf04 <- fore2020(afr_f04)
fore_amrf04 <- fore2020(amr_f04)
fore_emrf04 <- fore2020(emr_f04)
fore_eurf04 <- fore2020(eur_f04)
fore_seaf04 <- fore2020(sea_f04)
fore_wprf04 <- fore2020(wpr_f04)
bound_f04 <- cbind(fore_afrf04, fore_amrf04$Number, fore_emrf04$Number, fore_eurf04$Number, fore_seaf04$Number, fore_wprf04$Number)
colnames(bound_f04) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_f04 <- bound_f04[, c(2, 1, 3:7)]
#males 514
fore_afrm514 <- fore2020(afr_m514)
fore_amrm514 <- fore2020(amr_m514)
fore_emrm514 <- fore2020(emr_m514)
fore_eurm514 <- fore2020(eur_m514)
fore_seam514 <- fore2020(sea_m514)
fore_wprm514 <- fore2020(wpr_m514)
bound_m514 <- cbind(fore_afrm514, fore_amrm514$Number, fore_emrm514$Number, fore_eurm514$Number, fore_seam514$Number, fore_wprm514$Number)
colnames(bound_m514) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_m514 <- bound_m514[, c(2, 1, 3:7)]
#females 514
fore_afrf514 <- fore2020(afr_f514)
fore_amrf514 <- fore2020(amr_f514)
fore_emrf514 <- fore2020(emr_f514)
fore_eurf514 <- fore2020(eur_f514)
fore_seaf514 <- fore2020(sea_f514)
fore_wprf514 <- fore2020(wpr_f514)
bound_f514 <- cbind(fore_afrf514, fore_amrf514$Number, fore_emrf514$Number, fore_eurf514$Number, fore_seaf514$Number, fore_wprf514$Number)
colnames(bound_f514) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_f514 <- bound_f514[, c(2, 1, 3:7)]
#males 15plus
fore_afrm15plus <- fore2020(afr_m15plus)
fore_amrm15plus <- fore2020(amr_m15plus)
fore_emrm15plus <- fore2020(emr_m15plus)
fore_eurm15plus <- fore2020(eur_m15plus)
fore_seam15plus <- fore2020(sea_m15plus)
fore_wprm15plus <- fore2020(wpr_m15plus)
bound_m15plus <- cbind(fore_afrm15plus, fore_amrm15plus$Number, fore_emrm15plus$Number, fore_eurm15plus$Number, fore_seam15plus$Number, fore_wprm15plus$Number)
colnames(bound_m15plus) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_m15plus <- bound_m15plus[, c(2, 1, 3:7)]
#females 15plus
fore_afrf15plus <- fore2020(afr_f15plus)
fore_amrf15plus <- fore2020(amr_f15plus)
fore_emrf15plus <- fore2020(emr_f15plus)
fore_eurf15plus <- fore2020(eur_f15plus)
fore_seaf15plus <- fore2020(sea_f15plus)
fore_wprf15plus <- fore2020(wpr_f15plus)
bound_f15plus <- cbind(fore_afrf15plus, fore_amrf15plus$Number, fore_emrf15plus$Number, fore_eurf15plus$Number, fore_seaf15plus$Number, fore_wprf15plus$Number)
colnames(bound_f15plus) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_f15plus <- bound_f15plus[, c(2, 1, 3:7)]
Attempt 2 at creating the time series analyses
#create all tables
test_males04
Warning: Removed 6 row(s) containing missing values (geom_path).
test_females04Warning: Removed 6 row(s) containing missing values (geom_path).
test_males514Warning: Removed 6 row(s) containing missing values (geom_path).
test_females514Warning: Removed 6 row(s) containing missing values (geom_path).
test_males15plusWarning: Removed 6 row(s) containing missing values (geom_path).
test_females15plusWarning: Removed 6 row(s) containing missing values (geom_path).
Creating notification graphs for entire world
#Putting together notification data for years 2013 to 2020
world_data <- world_2013[,-c(1:3)] %>% group_by(year) %>% na.omit()
tot_m04 <- summarise(world_data, total = sum(newrel_m04))
tot_m04 %<>% as.data.frame()
colnames(tot_m04) <- c("year", "m04")
tot_f04 <- summarise(world_data, total = sum(newrel_f04))
tot_f04 %<>% as.data.frame()
colnames(tot_f04) <- c("year", "f04")
tot_m514 <- summarise(world_data, total = sum(newrel_m514))
tot_m514 %<>% as.data.frame()
colnames(tot_m514) <- c("year", "m514")
tot_f514 <- summarise(world_data, total = sum(newrel_f514))
tot_f514 %<>% as.data.frame()
colnames(tot_f514) <- c("year", "f514")
tot_m15plus <- summarise(world_data, total = sum(newrel_m15plus))
tot_m15plus %<>% as.data.frame()
colnames(tot_m15plus) <- c("year", "m15plus")
tot_f15plus <- summarise(world_data, total = sum(newrel_f15plus))
tot_f15plus %<>% as.data.frame()
colnames(tot_f15plus) <- c("year", "f15plus")
all_tots <- cbind(tot_m04, tot_f04$f04, tot_m514$m514, tot_f514$f514, tot_m15plus$m15plus, tot_f15plus$f15plus)
colnames(all_tots) <- c("year", "case_m04", "case_f04", "case_m514", "case_f514", "case_m15plus", "case_f15plus")
all_tots <- mutate(all_tots, "all04" = case_m04 + case_f04, "all514" = case_m514 + case_f514, "all15plus" = case_m15plus + case_f15plus, "all_male" = case_m04 + case_m514 + case_m15plus, "all_female" = case_f04 + case_f514 + case_f15plus, "all_cases" = all_male + all_female)
all_copy <- all_tots
all_copy04 <- all_copy[, c(1:3)]
colnames(all_copy04) <- c("year", "male", "female")
all_copy04 <- pivot_longer(all_copy04, cols = c(male, female), names_to = "sex", values_to = "cases")
all_copy514 <- all_copy[, c(1,4,5)]
colnames(all_copy514) <- c("year", "male", "female")
all_copy514 <- pivot_longer(all_copy514, cols = c(male, female), names_to = "sex", values_to = "cases")
all_copy15plus <- all_copy[, c(1,6,7)]
colnames(all_copy15plus) <- c("year", "male", "female")
all_copy15plus <- pivot_longer(all_copy15plus, cols = c(male, female), names_to = "sex", values_to = "cases")
all_copy15plus <- all_copy[, c(1,6,7)]
colnames(all_copy15plus) <- c("year", "male", "female")
all_copy15plus <- pivot_longer(all_copy15plus, cols = c(male, female), names_to = "sex", values_to = "cases")
all_copyall <- all_copy[, c(1,11,12)]
colnames(all_copyall) <- c("year", "male", "female")
all_copyall <- pivot_longer(all_copyall, cols = c(male, female), names_to = "sex", values_to = "cases")
###Drawing the plots
world_04 <- ggplot(all_copy04, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst 0-4 yrs from 2013 to 2020")
world_514 <- ggplot(all_copy514, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst 5-14 yrs from 2013 to 2020")
world_15plus <- ggplot(all_copy15plus, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst 15plus yrs from 2013 to 2020")
world_all <- ggplot(all_copyall, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst All Ages from 2013 to 2020")
Repeating the continental difference between actual and predicted and including column for overall
#extracting data for whole world
summed_m04 <- tot_world %>% group_by(year) %>% na.omit()
summed_m04 <- summarise(summed_m04, total = sum(newrel_m04))
colnames(summed_m04) <- c("year", "m04")
summed_f04 <- tot_world %>% group_by(year) %>% na.omit()
summed_f04 <- summarise(summed_f04, total = sum(newrel_f04))
colnames(summed_f04) <- c("year", "f04")
summed_m514 <- tot_world %>% group_by(year) %>% na.omit()
summed_m514 <- summarise(summed_m514, total = sum(newrel_m514))
colnames(summed_m514) <- c("year", "m514")
summed_f514 <- tot_world %>% group_by(year) %>% na.omit()
summed_f514 <- summarise(summed_f514, total = sum(newrel_f514))
colnames(summed_f514) <- c("year", "f514")
summed_m15plus <- tot_world %>% group_by(year) %>% na.omit()
summed_m15plus <- summarise(summed_m15plus, total = sum(newrel_m15plus))
colnames(summed_m15plus) <- c("year", "m15plus")
summed_f15plus <- tot_world %>% group_by(year) %>% na.omit()
summed_f15plus <- summarise(summed_f15plus, total = sum(newrel_f15plus))
colnames(summed_f15plus) <- c("year", "f15plus")
all_summed <- cbind(summed_m04, summed_f04$f04, summed_m514$m514, summed_f514$f514, summed_m15plus$m15plus, summed_f15plus$f15plus)
colnames(all_summed) <- c("year", "m04", "f04", "m514", "f514", "m15plus", "f15plus")
##Attempt at creating forecast
arima_allm04 <- all_summed[-8, 2]
ts_arimam04 <- ts(arima_allm04, start = 2013, frequency = 1)
ts_arimam04 %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_m04 <- forecast(ts_arimam04, h = 1)
autoplot(forecast_m04)
sum_m04 <- print(summary(forecast_m04))
arima_allf04 <- all_summed[-8, 3]
ts_arimaf04 <- ts(arima_allf04, start = 2013, frequency = 1)
ts_arimaf04 %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_f04 <- forecast(ts_arimaf04, h = 1)
autoplot(forecast_f04)
sum_f04 <- print(summary(forecast_f04))
arima_allm514 <- all_summed[-8, 4]
ts_arimam514 <- ts(arima_allm514, start = 2013, frequency = 1)
ts_arimam514 %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_m514 <- forecast(ts_arimam514, h = 1)
autoplot(forecast_m514)
sum_m514 <- print(summary(forecast_m514))
arima_allf514 <- all_summed[-8, 5]
ts_arimaf514 <- ts(arima_allf514, start = 2013, frequency = 1)
ts_arimaf514 %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_f514 <- forecast(ts_arimaf514, h = 1)
autoplot(forecast_f514)
sum_f514 <- print(summary(forecast_f514))
arima_allm15plus <- all_summed[-8, 6]
ts_arimam15plus <- ts(arima_allm15plus, start = 2013, frequency = 1)
ts_arimam15plus %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_m15plus <- forecast(ts_arimam15plus, h = 1)
autoplot(forecast_m15plus)
sum_m15plus <- print(summary(forecast_m15plus))
arima_allf15plus <- all_summed[-8, 7]
ts_arimaf15plus <- ts(arima_allf15plus, start = 2013, frequency = 1)
ts_arimaf15plus %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_f15plus <- forecast(ts_arimaf15plus, h = 1)
autoplot(forecast_f15plus)
sum_f15plus <- print(summary(forecast_f15plus))
##Creating single data frame with best estimates for each group
best_estimates <- cbind(sum_m04$`Point Forecast`, sum_f04$`Point Forecast`, sum_m514$`Point Forecast`, sum_f514$`Point Forecast`, sum_m15plus$`Point Forecast`, sum_f15plus$`Point Forecast`)
colnames(best_estimates) <- c("m04", "f04", "m514", "f514", "m15plus", "f15plus")
all_compared <- rbind(all_summed[8,-1], best_estimates)
rownames(all_compared) <- c("Actual", "Predicted")
all_differences <- t(all_compared)
all_differences %<>% as.data.frame()
all_differences <- mutate(all_differences, Percentage = 100*((Actual-Predicted)/Predicted))
sex <- c("m", "f")
add_col1 <- cbind(all_differences, sex)
age_group <- c("04", "04", "514", "514", "15plus", "15plus")
add_col2 <- cbind(add_col1, age_group)
g_whoregion <- "ALL"
add_col3 <- cbind(add_col2, g_whoregion)
#Ready to merge
ready_all <- add_col3[, c(5,4,6,3)]
colnames(ready_all)[colnames(ready_all) == "Percentage"] <- "value"
piv_four <- rbind(piv_three, ready_all)
piv_four <- piv_four %>% arrange(age_group) %>% group_by(age_group) %>% arrange(sex)
piv_four$g_whoregion <- factor(piv_four$g_whoregion, levels = c("AFR", "AMR", "EMR", "EUR", "SEA", "WPR", "ALL"))
arima_four <- ggplot(piv_four, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_four$age_group) + geom_bar(stat = "identity", position = "dodge") + theme(axis.text.x = element_text(angle = 90)) + theme(legend.title = element_blank()) + xlab("Region") + ylab("Percentage Difference Between Actual and Predicted Cases in 2020") + labs(title = "Difference Between Actual and Predicted Notifications in 2020")
arima_four ---
title: "All Countries Regional Data"
output: html_notebook
---

AIM: Create region by region analysis of male vs female for each age groups (0-4, 5-14, 0-14 and 15 plus) for all countries separated by region

```{r}
setwd("~/Desktop/AFP/modV3/data")
library(dplyr)
library(tidyverse)
library(tidyr)
library(forecast)
library(fpp2)
library(stringr)
library(magrittr)
library(ggplot2)

world <- read_csv("TB_notifications.csv")
tidyworld <- world[, c(1,3,5,6,100,103,104,113,115,118,119,128)]
world_2013 <- tidyworld %>% filter(year > 2012)
```

Organising the data by g_whoregion

```{r}
grouped_2013 <- group_by(world_2013, g_whoregion) %>% filter(year == 2013) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2013)

grouped_2014 <- group_by(world_2013, g_whoregion) %>% filter(year == 2014) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2014)

grouped_2015 <- group_by(world_2013, g_whoregion) %>% filter(year == 2015) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2015)

grouped_2016 <- group_by(world_2013, g_whoregion) %>% filter(year == 2016) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2016)

grouped_2017 <- group_by(world_2013, g_whoregion) %>% filter(year == 2017) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2017)

grouped_2018 <- group_by(world_2013, g_whoregion) %>% filter(year == 2018) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2018)

grouped_2019 <- group_by(world_2013, g_whoregion) %>% filter(year == 2019) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2019)

grouped_2020 <- group_by(world_2013, g_whoregion) %>% filter(year == 2020) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2020)

bind_world <- rbind(grouped_2013, grouped_2014, grouped_2015, grouped_2016, grouped_2017, grouped_2018, grouped_2019, grouped_2020)

bind_world <- bind_world[, c(1, 10, 2:9)]

arrange_world <- arrange(bind_world, g_whoregion)

##Including columns for total values 

mutate_world <- mutate(arrange_world, "newrel_tot04" = newrel_m04 + newrel_f04, "newrel_tot514" = newrel_m514 + newrel_f514, "newrel_tot014" = newrel_m014 + newrel_f014, "newrel_tot15plus" = newrel_m15plus + newrel_f15plus)

perc_world <- mutate(mutate_world, "perc_014" = 100 * (newrel_tot014/(newrel_tot014 + newrel_tot15plus)), "perc_youngkids" = 100 * (newrel_tot04/newrel_tot014))



```

Creating graphs for male vs female for 0-4 by g_whoregion

```{r}
afr_04 <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m04, newrel_f04) %>% 
  t()
colnames(afr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_04 <- afr_04[, -8]
afr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,30000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in AFR")

amr_04 <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m04, newrel_f04) %>% 
  t()
colnames(amr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_04 <- amr_04[, -8]
amr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,3000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in AMR")

emr_04 <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m04, newrel_f04) %>% 
  t()
colnames(emr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_04 <- emr_04[, -8]
emr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,20000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in EMR")

eur_04 <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m04, newrel_f04) %>% 
  t()
colnames(eur_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_04 <- eur_04[, -8]
eur_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,3000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in EUR")

sea_04 <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m04, newrel_f04) %>% 
  t() 
colnames(sea_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_04 <- sea_04[, -8]
sea_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,60000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in SEA")

wpr_04 <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m04, newrel_f04) %>% 
  t()
colnames(wpr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_04 <- wpr_04[, -8]
wpr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,20000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in WPR")


```
Creating graphs for notifications in 5-14 years by g_whoregion

```{r}
afr_514 <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m514, newrel_f514) %>% 
  t() 
colnames(afr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_514 <- afr_514[, -8]
afr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,40000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-514 years in AFR")

amr_514 <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m514, newrel_f514) %>% 
  t() 
colnames(amr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_514 <- amr_514[, -8]
amr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,4000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in AMR")

emr_514 <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m514, newrel_f514) %>% 
  t() 
colnames(emr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_514 <- emr_514[, -8]
emr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,30000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in EMR")

eur_514 <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m514, newrel_f514) %>% 
  t()
colnames(eur_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_514 <- eur_514[, -8]
eur_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,5000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in EUR")

sea_514 <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m514, newrel_f514) %>% 
  t() 
colnames(sea_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_514 <- sea_514[, -8]
sea_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,100000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in SEA")

wpr_514 <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m514, newrel_f514) %>% 
  t()
colnames(wpr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_514 <- wpr_514[, -8]
wpr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,30000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in WPR")
```
Creating plot for 0-14 

```{r}
afr_014 <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m014, newrel_f014) %>% 
  t()
colnames(afr_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,70000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in AFR")

amr_014 <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m014, newrel_f014) %>% 
  t()
colnames(amr_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,6000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in AMR")

emr_014 <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m014, newrel_f014) %>% 
  t() 
colnames(emr_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,50000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in EMR")

eur_014 <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m014, newrel_f014) %>% 
  t() 
colnames(eur_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,8000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in EUR")

sea_014 <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m014, newrel_f014) %>% 
  t()
colnames(sea_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,150000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in SEA")

wpr_014 <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m014, newrel_f014) %>% 
  t() 
colnames(wpr_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,50000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in WPR")
```
Creating plot for 15 plus 

```{r}
afr_15plus <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t() 
colnames(afr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_15plus <- afr_15plus[, -8]
afr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in AFR")

amr_15plus <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t()
colnames(amr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_15plus <- amr_15plus[, -8]
amr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,300000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15 plus years in AMR")

emr_15plus <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t() 
colnames(emr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_15plus <- emr_15plus[, -8]
emr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,500000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in EMR")

eur_15plus <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t() 
colnames(eur_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_15plus <- eur_15plus[, -8]
eur_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,200000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in EUR")

sea_15plus <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t() 
colnames(sea_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_15plus <- sea_15plus[, -8]
sea_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,2000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in SEA")

wpr_15plus <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t() 
colnames(wpr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_15plus <- wpr_15plus[, -8]
wpr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in WPR")
```

```{r}
#Creating barplot data for whole world 
whole_world <- perc_world %>% filter(year == "2013") %>% select(-c("g_whoregion", "year")) %>% lapply(., FUN = sum) %>% as.data.frame()

all_func <- function(df = perc_world, yr){
        C <- df %>% filter(year == yr) %>% select(-c("g_whoregion", "year", "perc_014", "perc_youngkids")) %>% lapply(., FUN = sum) %>% as.data.frame()
        return(C) }

years <- c(2013:2020)
whole_world_data  <- NULL
for(i in years){
        output <- all_func(perc_world, i)
        whole_world_data <- rbind(whole_world_data, output) }

whole_world_data <- cbind(whole_world_data, years)
whole_world_data <- whole_world_data[, c(13, 1:12)]

male_world <- whole_world_data %>% select(contains("m")) %>% as.data.frame() %>% mutate(m_total = newrel_m014 + newrel_m15plus)
        
female_world <- whole_world_data %>% select(contains("f")) %>% as.data.frame() %>% mutate(f_total = newrel_f014 + newrel_f15plus)

mf_years <- cbind(male_world$m_total, female_world$f_total)
colnames(mf_years) <- c("m_total", "f_total")
mf_years <- t(mf_years)
colnames(mf_years) <- c(2013:2020)
mf_years <- mf_years[, c(1:7)]

mf_plot <- mf_years %>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,5000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages globally")


```



Plotting total notifications over time 

```{r}
tot_world <- mutate(perc_world, "newrel_mtot" = newrel_m014 + newrel_m15plus, "newrel_ftot" = newrel_f014 + newrel_f15plus, "TOT" = newrel_mtot + newrel_ftot)

afr_tot <- tot_world %>% filter(g_whoregion == "AFR") %>% select(newrel_mtot, newrel_ftot) %>% 
  t() 
colnames(afr_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in AFR")

amr_tot <- tot_world %>% filter(g_whoregion == "AMR") %>% select(newrel_mtot, newrel_ftot) %>% 
  t()
colnames(amr_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,200000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in AMR")

emr_tot <- tot_world %>% filter(g_whoregion == "EMR") %>% select(newrel_mtot, newrel_ftot) %>% 
  t() 
colnames(emr_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,500000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in EMR")

eur_tot <- tot_world %>% filter(g_whoregion == "EUR") %>% select(newrel_mtot, newrel_ftot) %>% 
  t()
colnames(eur_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,200000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in EUR")

sea_tot <- tot_world %>% filter(g_whoregion == "SEA") %>% select(newrel_mtot, newrel_ftot) %>% 
  t() 
colnames(sea_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,3000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in SEA")

wpr_tot <- tot_world %>% filter(g_whoregion == "WPR") %>% select(newrel_mtot, newrel_ftot) %>% 
  t() 
colnames(wpr_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1500000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in WPR")

```
```{r}
##Attempt at creating a forecast 

filt_afr04 <- perc_world %>% filter(g_whoregion == "AFR") %>% filter(year != 2020)

ts_afr04 <- ts(filt_afr04[, c(3)], start = 2013, frequency = 1)

arima_afr04 <- auto.arima(ts_afr04, d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
print(summary(arima_afr04))
checkresiduals(arima_afr04)

fcst_afrm04 <- forecast(arima_afr04, h = 1)
autoplot(fcst_afrm04)
sum_afrm04 <- print(summary(fcst))

####Attempt at creating function to produce predictions 

out_2020 <- function(df = tot_world, region){
        df %>% filter(year != 2020) %>% filter(g_whoregion == region) %>% return() }

arima_TB <- function(df = tot_world, group) {
        data1 <- df %>% select(group) %>% ts(start = 2013, frequency = 1) %>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE) 
        print(summary(data1))
        FCST <- forecast(data1, h = 1)
        autoplot(FCST)
        print(summary(FCST))}

##AFRICA ESTIMATES 

afr_m04 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m04") 
rownames(afr_m04) <- "afr_m04"

afr_f04 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f04")
rownames(afr_f04) <- "afr_f04"

afr_m514 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m514")
rownames(afr_m514) <- "afr_m514"

afr_f514 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f514")
rownames(afr_f514) <- "afr_f514"

afr_m014 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m014")
rownames(afr_m014) <- "afr_m014"

afr_f014 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f014")
rownames(afr_f014) <- "afr_f014"

afr_m15plus <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m15plus")
rownames(afr_m15plus) <- "afr_m15plus"

afr_f15plus <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f15plus")
rownames(afr_f15plus) <- "afr_f15plus"

afr_estimates <- rbind(afr_m04, afr_f04, afr_m514, afr_f514, afr_m014, afr_f014, afr_m15plus, afr_f15plus)


#SEA predictions 
sea_m04 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m04") 
rownames(sea_m04) <- "sea_m04"

sea_f04 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f04")
rownames(sea_f04) <- "sea_f04"

sea_m514 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m514")
rownames(sea_m514) <- "sea_m514"

sea_f514 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f514")
rownames(sea_f514) <- "sea_f514"

sea_m014 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m014")
rownames(sea_m014) <- "sea_m014"

sea_f014 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f014")
rownames(sea_f014) <- "sea_f014"

sea_m15plus <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m15plus")
rownames(sea_m15plus) <- "sea_m15plus"

sea_f15plus <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f15plus")
rownames(sea_f15plus) <- "sea_f15plus"

sea_estimates <- rbind(sea_m04, sea_f04, sea_m514, sea_f514, sea_m014, sea_f014, sea_m15plus, sea_f15plus)

#WPR estimates 

wpr_m04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m04") 
rownames(wpr_m04) <- "wpr_m04"

wpr_f04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f04")
rownames(wpr_f04) <- "wpr_f04"

wpr_m514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m514")
rownames(wpr_m514) <- "wpr_m514"

wpr_f514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f514")
rownames(wpr_f514) <- "wpr_f514"

wpr_m014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m014")
rownames(wpr_m014) <- "wpr_m014"

wpr_f014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f014")
rownames(wpr_f014) <- "wpr_f014"

wpr_m15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m15plus")
rownames(wpr_m15plus) <- "wpr_m15plus"

wpr_f15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f15plus")
rownames(wpr_f15plus) <- "wpr_f15plus"

wpr_estimates <- rbind(wpr_m04, wpr_f04, wpr_m514, wpr_f514, wpr_m014, wpr_f014, wpr_m15plus, wpr_f15plus)

#EUR Estimates

eur_m04 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m04") 
rownames(eur_m04) <- "eur_m04"

eur_f04 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f04")
rownames(eur_f04) <- "eur_f04"

eur_m514 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m514")
rownames(eur_m514) <- "eur_m514"

eur_f514 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f514")
rownames(eur_f514) <- "eur_f514"

eur_m014 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m014")
rownames(eur_m014) <- "eur_m014"

eur_f014 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f014")
rownames(eur_f014) <- "eur_f014"

eur_m15plus <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m15plus")
rownames(eur_m15plus) <- "eur_m15plus"

eur_f15plus <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f15plus")
rownames(eur_f15plus) <- "eur_f15plus"

eur_estimates <- rbind(eur_m04, eur_f04, eur_m514, eur_f514, eur_m014, eur_f014, eur_m15plus, eur_f15plus)

#AMR Estimates

emr_m04 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m04") 
rownames(emr_m04) <- "emr_m04"

emr_f04 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f04")
rownames(emr_f04) <- "emr_f04"

emr_m514 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m514")
rownames(emr_m514) <- "emr_m514"

emr_f514 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f514")
rownames(emr_f514) <- "emr_f514"

emr_m014 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m014")
rownames(emr_m014) <- "emr_m014"

emr_f014 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f014")
rownames(emr_f014) <- "emr_f014"

emr_m15plus <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m15plus")
rownames(emr_m15plus) <- "emr_m15plus"

emr_f15plus <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f15plus")
rownames(emr_f15plus) <- "emr_f15plus"

emr_estimates <- rbind(emr_m04, emr_f04, emr_m514, emr_f514, emr_m014, emr_f014, emr_m15plus, emr_f15plus)

#WPR Estimates

wpr_m04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m04") 
rownames(wpr_m04) <- "wpr_m04"

wpr_f04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f04")
rownames(wpr_f04) <- "wpr_f04"

wpr_m514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m514")
rownames(wpr_m514) <- "wpr_m514"

wpr_f514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f514")
rownames(wpr_f514) <- "wpr_f514"

wpr_m014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m014")
rownames(wpr_m014) <- "wpr_m014"

wpr_f014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f014")
rownames(wpr_f014) <- "wpr_f014"

wpr_m15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m15plus")
rownames(wpr_m15plus) <- "wpr_m15plus"

wpr_f15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f15plus")
rownames(wpr_f15plus) <- "wpr_f15plus"

wpr_estimates <- rbind(wpr_m04, wpr_f04, wpr_m514, wpr_f514, wpr_m014, wpr_f014, wpr_m15plus, wpr_f15plus)

#AMR Estimates

amr_m04 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m04") 
rownames(amr_m04) <- "amr_m04"

amr_f04 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f04")
rownames(amr_f04) <- "amr_f04"

amr_m514 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m514")
rownames(amr_m514) <- "amr_m514"

amr_f514 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f514")
rownames(amr_f514) <- "amr_f514"

amr_m014 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m014")
rownames(amr_m014) <- "amr_m014"

amr_f014 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f014")
rownames(amr_f014) <- "amr_f014"

amr_m15plus <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m15plus")
rownames(amr_m15plus) <- "amr_m15plus"

amr_f15plus <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f15plus")
rownames(amr_f15plus) <- "amr_f15plus"

amr_estimates <- rbind(amr_m04, amr_f04, amr_m514, amr_f514, amr_m014, amr_f014, amr_m15plus, amr_f15plus)

```
Combining the data for 2020 with the model estimates 

```{r}
##AFR difference calculations 
afr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "AFR") %>% t() 
afr_2020 <- as.data.frame(afr_2020[c(3:10), ])

rownames(afr_2020) <- c("afr_m04", "afr_f04", "afr_m514", "afr_f514", "afr_m014", "afr_f014", "afr_m15plus", "afr_f15plus")
colnames(afr_2020) <- "notif_2020"

est_afr_2020 <- cbind(afr_2020, afr_estimates)
colnames(est_afr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_afr_2020$notif_2020 <- as.numeric(est_afr_2020$notif_2020)

dif_afr <- mutate(est_afr_2020, "Difference" = notif_2020 - est_2020, "afr_perc" = 100*(Difference/est_2020))

##SEA difference calculations 
sea_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "SEA") %>% t() 
sea_2020 <- as.data.frame(sea_2020[c(3:10), ])

rownames(sea_2020) <- c("sea_m04", "sea_f04", "sea_m514", "sea_f514", "sea_m014", "sea_f014", "sea_m15plus", "sea_f15plus")
colnames(sea_2020) <- "notif_2020"
est_sea_2020 <- cbind(sea_2020, sea_estimates)
colnames(est_sea_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_sea_2020$notif_2020 <- as.numeric(est_sea_2020$notif_2020)

dif_sea <- mutate(est_sea_2020, "Difference" = notif_2020 - est_2020, "sea_perc" = 100*(Difference/est_2020))

#WPR difference calculations 
wpr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "WPR") %>% t() 
wpr_2020 <- as.data.frame(wpr_2020[c(3:10), ])

rownames(wpr_2020) <- c("wpr_m04", "wpr_f04", "wpr_m514", "wpr_f514", "wpr_m014", "wpr_f014", "wpr_m15plus", "wpr_f15plus")
colnames(wpr_2020) <- "wpr_2020"
est_wpr_2020 <- cbind(wpr_2020, wpr_estimates)
colnames(est_wpr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_wpr_2020$notif_2020 <- as.numeric(est_wpr_2020$notif_2020)

dif_wpr <- mutate(est_wpr_2020, "Difference" = notif_2020 - est_2020, "wpr_perc" = 100*(Difference/est_2020))

#EUR difference calculations 
eur_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "EUR") %>% t() 
eur_2020 <- as.data.frame(eur_2020[c(3:10), ])

rownames(eur_2020) <- c("eur_m04", "eur_f04", "eur_m514", "eur_f514", "eur_m014", "eur_f014", "eur_m15plus", "eur_f15plus")
colnames(eur_2020) <- "eur_2020"
est_eur_2020 <- cbind(eur_2020, eur_estimates)
colnames(est_eur_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_eur_2020$notif_2020 <- as.numeric(est_eur_2020$notif_2020)

dif_eur <- mutate(est_eur_2020, "Difference" = notif_2020 - est_2020, "eur_perc" = 100*(Difference/est_2020)) 

#AMR difference calculations
amr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "AMR") %>% t() 
amr_2020 <- as.data.frame(amr_2020[c(3:10), ])

rownames(amr_2020) <- c("amr_m04", "amr_f04", "amr_m514", "amr_f514", "amr_m014", "amr_f014", "amr_m15plus", "amr_f15plus")
colnames(eur_2020) <- "amr_2020"
est_amr_2020 <- cbind(amr_2020, amr_estimates)
colnames(est_amr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_amr_2020$notif_2020 <- as.numeric(est_amr_2020$notif_2020)

dif_amr <- mutate(est_amr_2020, "Difference" = notif_2020 - est_2020, "amr_perc" = 100*(Difference/est_2020)) 

#EMR difference calculations
emr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "EMR") %>% t() 
emr_2020 <- as.data.frame(emr_2020[c(3:10), ])

rownames(emr_2020) <- c("emr_m04", "emr_f04", "emr_m514", "emr_f514", "emr_m014", "emr_f014", "emr_m15plus", "emr_f15plus")
colnames(emr_2020) <- "emr_2020"
est_emr_2020 <- cbind(emr_2020, emr_estimates)
colnames(est_emr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_emr_2020$notif_2020 <- as.numeric(est_emr_2020$notif_2020)

dif_emr <- mutate(est_emr_2020, "Difference" = notif_2020 - est_2020, "emr_perc" = 100*(Difference/est_2020)) 

#Combined data frame with difference data 
combined_dif <- data.frame(dif_afr[, c(7,8)], dif_amr[,c(7,8)], dif_emr[, c(7,8)], dif_eur[,c(7:8)], dif_sea[,c(7:8)], dif_wpr[,c(7:8)])
combined_dif$group <- as.vector(rownames(combined_dif))
combined_dif$group <- c("m04", "f04", "m514", "f514", "m014", "f014", "m15plus", "f15plus")
rownames(combined_dif) <- c(1:8)
combined_dif <- combined_dif[, c(13,1:12)]

colnames(combined_dif) <- c("group", "AFR_dif", "AFR_perc", "AMR_dif", "AMR_perc", "EMR_dif", "EMR_perc", "EUR_dif", "EUR_perc", "SEA_dif", "SEA_perc", "WPR_dif", "WPR_perc")

#isolating only the percentage data to be able to plot a bar plot 
bar_data <- combined_dif[, c(1,3,5,7,9,11,13)]
bar_newgroup <- str_split_fixed(bar_data$group, "", 2)
bar_data2 <- cbind(bar_data, bar_newgroup)
colnames(bar_data2) <- c("group", "AFR", "AMR", "EMR", "EUR", "SEA", "WPR", "sex", "age_group")
bar_data2 <- bar_data2[, c(9,8,2:7)]


piv_dif <- pivot_longer(bar_data2, cols = c("AFR", "AMR", "EMR", "EUR", "SEA", "WPR"), names_to = "g_whoregion", values_to = "value")

library(dplyr)
library(ggplot2)
piv_three <- filter(piv_dif, age_group != "014")

##Attempt at drawing plots  including 0-14 
arima_dif <- ggplot(piv_dif, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_dif$age_group) + geom_bar(stat = "identity", position = "dodge")

##Attempt excluding 0-14 
piv_copy <- piv_three
piv_copy$age_group <- factor(piv_copy$age_group, levels = c("04", "514", "15plus"))
arima_copy <- ggplot(piv_copy, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_copy$age_group) + geom_bar(stat = "identity", position = "dodge")
arima_copy

arima_three <- ggplot(piv_three, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_three$age_group) + geom_bar(stat = "identity", position = "dodge")
arima_three

``` 



```{r}
#Attempt at the forecasting models 
#Rearranging data for 0-4 males 

males_04 <- select(perc_world, c(g_whoregion, year, newrel_m04)) 
piv_na <- pivot_wider(males_04, names_from = "year", values_from = "newrel_m04")
piv_na$`2020` <- NA
males_back <- pivot_longer(piv_na, cols = c(as.vector(colnames(piv_na[,2:9]))), names_to = "year")
males_back$year <- as.numeric(males_back$year)
males_no_pivot <- males_04
males_pivot <- pivot_wider(males_04, names_from = "g_whoregion", values_from = "newrel_m04")

select(afr_m04, "Point Forecast")

fore2020 <- function(df){
  a <- select(df, "Point Forecast")
  b <- "2020"
  colnames(a) <- "Number"
  rownames(a) <- NULL
  a$Year <- b
  return(a)
}

#males 04 
fore_afrm04 <- fore2020(afr_m04)
fore_amrm04 <- fore2020(amr_m04)
fore_emrm04 <- fore2020(emr_m04)
fore_eurm04 <- fore2020(eur_m04)
fore_seam04 <- fore2020(sea_m04)
fore_wprm04 <- fore2020(wpr_m04)

bound_fore <- cbind(fore_afrm04, fore_amrm04$Number, fore_emrm04$Number, fore_eurm04$Number, fore_seam04$Number, fore_wprm04$Number)

colnames(bound_fore) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_fore <- bound_fore[, c(2, 1, 3:7)]

#females04
fore_afrf04 <- fore2020(afr_f04)
fore_amrf04 <- fore2020(amr_f04)
fore_emrf04 <- fore2020(emr_f04)
fore_eurf04 <- fore2020(eur_f04)
fore_seaf04 <- fore2020(sea_f04)
fore_wprf04 <- fore2020(wpr_f04)

bound_f04 <- cbind(fore_afrf04, fore_amrf04$Number, fore_emrf04$Number, fore_eurf04$Number, fore_seaf04$Number, fore_wprf04$Number)

colnames(bound_f04) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_f04 <- bound_f04[, c(2, 1, 3:7)]

#males 514 
fore_afrm514 <- fore2020(afr_m514)
fore_amrm514 <- fore2020(amr_m514)
fore_emrm514 <- fore2020(emr_m514)
fore_eurm514 <- fore2020(eur_m514)
fore_seam514 <- fore2020(sea_m514)
fore_wprm514 <- fore2020(wpr_m514)

bound_m514 <- cbind(fore_afrm514, fore_amrm514$Number, fore_emrm514$Number, fore_eurm514$Number, fore_seam514$Number, fore_wprm514$Number)

colnames(bound_m514) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_m514 <- bound_m514[, c(2, 1, 3:7)]

#females 514 
fore_afrf514 <- fore2020(afr_f514)
fore_amrf514 <- fore2020(amr_f514)
fore_emrf514 <- fore2020(emr_f514)
fore_eurf514 <- fore2020(eur_f514)
fore_seaf514 <- fore2020(sea_f514)
fore_wprf514 <- fore2020(wpr_f514)

bound_f514 <- cbind(fore_afrf514, fore_amrf514$Number, fore_emrf514$Number, fore_eurf514$Number, fore_seaf514$Number, fore_wprf514$Number)

colnames(bound_f514) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_f514 <- bound_f514[, c(2, 1, 3:7)]

#males 15plus 
fore_afrm15plus <- fore2020(afr_m15plus)
fore_amrm15plus <- fore2020(amr_m15plus)
fore_emrm15plus <- fore2020(emr_m15plus)
fore_eurm15plus <- fore2020(eur_m15plus)
fore_seam15plus <- fore2020(sea_m15plus)
fore_wprm15plus <- fore2020(wpr_m15plus)

bound_m15plus <- cbind(fore_afrm15plus, fore_amrm15plus$Number, fore_emrm15plus$Number, fore_eurm15plus$Number, fore_seam15plus$Number, fore_wprm15plus$Number)

colnames(bound_m15plus) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_m15plus <- bound_m15plus[, c(2, 1, 3:7)]

#females 15plus 
fore_afrf15plus <- fore2020(afr_f15plus)
fore_amrf15plus <- fore2020(amr_f15plus)
fore_emrf15plus <- fore2020(emr_f15plus)
fore_eurf15plus <- fore2020(eur_f15plus)
fore_seaf15plus <- fore2020(sea_f15plus)
fore_wprf15plus <- fore2020(wpr_f15plus)

bound_f15plus <- cbind(fore_afrf15plus, fore_amrf15plus$Number, fore_emrf15plus$Number, fore_eurf15plus$Number, fore_seaf15plus$Number, fore_wprf15plus$Number)

colnames(bound_f15plus) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_f15plus <- bound_f15plus[, c(2, 1, 3:7)]


```

Attempt 2 at creating the time series analyses 

```{r}
#males04 plot
males_04 <- select(perc_world, c(g_whoregion, year, newrel_m04)) 
males_04 <- mutate(males_04, newrel_m04 = ifelse(year == "2020", NA, newrel_m04))
gg_males04 <- ggplot(males_04, aes(x = year, y = newrel_m04, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 27043, col = "red") + geom_point(x = 2020, y = 2062, col = "brown") + geom_point(x = 2020, y = 17243.17, col = "green") + geom_point(x = 2020, y = 1465, col = "turquoise2") + geom_point(x = 2020, y = 58951.83, col = "blue") + geom_point(x = 2020, y = 12558, col = "pink") + scale_y_continuous(limits = c(0, 60000)) + labs(title = "Notifications Time Series for Males 0-4 yrs")


###Trial at new plot 
only_2019 <- perc_world %>% filter(year == 2019) %>% select(g_whoregion, year, newrel_m04)
colnames(only_2019) <- c("g_whoregion", "year", "value")
only_pred <- pivot_longer(bound_fore, cols = c("AFR", "AMR", "EMR", "EUR", "SEA", "WPR"))
only_pred <- only_pred[,c(2,1,3)]
only_pred$Year <- as.numeric(only_pred$Year)
colnames(only_pred) <- c("g_whoregion", "year", "value")
pred_2019 <- rbind(only_2019, only_pred)

#filter continent  
pull_region <- function(region){
  x <- pred_2019 %>% filter(g_whoregion == region)
  return(x)
}

###frame for real data from 2019 to 2020 for second line segment 
incl_2020 <- perc_world %>% filter(year == c(2019, 2020)) %>% select(g_whoregion, year, newrel_m04)
colnames(incl_2020) <- c("g_whoregion", "year", "value")

pull_real <- function(region){
  x <- incl_2020 %>% filter(g_whoregion == region)
  return(x)}

####New plot 
test_males04 <- ggplot() + geom_line(data = males_04, aes(x = year, y = newrel_m04, col = g_whoregion)) + geom_line(data = pull_region("AFR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pull_region("AMR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pull_region("EMR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pull_region("EUR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pull_region("SEA"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pull_region("WPR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pull_real("AFR"), aes(x = year, y = value)) + geom_line(data = pull_real("AMR"), aes(x = year, y = value)) + geom_line(data = pull_real("EMR"), aes(x = year, y = value)) + geom_line(data = pull_real("EUR"), aes(x = year, y = value)) + geom_line(data = pull_real("SEA"), aes(x = year, y = value)) + geom_line(data = pull_real("WPR"), aes(x = year, y = value))

#females 04 plot
females_04 <- select(perc_world, c(g_whoregion, year, newrel_f04)) 
females_04 <- mutate(females_04, newrel_f04 = ifelse(year == "2020", NA, newrel_f04))
gg_females04 <- ggplot(females_04, aes(x = year, y = newrel_f04, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 23790, col = "red") + geom_point(x = 2020, y = 1776, col = "brown") + geom_point(x = 2020, y = 13286.17, col = "green") + geom_point(x = 2020, y = 1269, col = "turquoise2") + geom_point(x = 2020, y = 43694, col = "blue") + geom_point(x = 2020, y = 10140, col = "pink") + scale_y_continuous(limits = c(0, 50000)) + labs(title = "Notifications Time Series for Females 0-4 yrs")

###Trial at new plot 
only19_f04 <- perc_world %>% filter(year == 2019) %>% select(g_whoregion, year, newrel_f04)
colnames(only19_f04) <- c("g_whoregion", "year", "value")
onlypr_f04 <- pivot_longer(bound_f04, cols = c("AFR", "AMR", "EMR", "EUR", "SEA", "WPR"))
onlypr_f04 <- onlypr_f04[,c(2,1,3)]
onlypr_f04$Year <- as.numeric(onlypr_f04$Year)
colnames(onlypr_f04) <- c("g_whoregion", "year", "value")
pred19_f04 <- rbind(only19_f04, onlypr_f04)

#filter continent  
pullreg_f04 <- function(region){
  x <- pred19_f04 %>% filter(g_whoregion == region)
  return(x)
}

###frame for real data from 2019 to 2020 for second line segment 
incl20_f04 <- perc_world %>% filter(year == c(2019, 2020)) %>% select(g_whoregion, year, newrel_f04)
colnames(incl20_f04) <- c("g_whoregion", "year", "value")

pullreal_f04 <- function(region){
  x <- incl20_f04 %>% filter(g_whoregion == region)
  return(x)}

####New plot 
test_females04 <- ggplot() + geom_line(data = females_04, aes(x = year, y = newrel_f04, col = g_whoregion)) + geom_line(data = pullreg_f04("AFR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_f04("AMR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_f04("EMR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_f04("EUR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_f04("SEA"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_f04("WPR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreal_f04("AFR"), aes(x = year, y = value)) + geom_line(data = pullreal_f04("AMR"), aes(x = year, y = value)) + geom_line(data = pullreal_f04("EMR"), aes(x = year, y = value)) + geom_line(data = pullreal_f04("EUR"), aes(x = year, y = value)) + geom_line(data = pullreal_f04("SEA"), aes(x = year, y = value)) + geom_line(data = pullreal_f04("WPR"), aes(x = year, y = value))

#males 514 plot 
males_514 <- select(perc_world, c(g_whoregion, year, newrel_m514)) 
males_514 <- mutate(males_514, newrel_m514 = ifelse(year == "2020", NA, newrel_m514))
gg_males514 <- ggplot(males_514, aes(x = year, y = newrel_m514, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 32352, col = "red") + geom_point(x = 2020, y = 2826, col = "brown") + geom_point(x = 2020, y = 18620, col = "green") + geom_point(x = 2020, y = 3057, col = "turquoise2") + geom_point(x = 2020, y = 82535, col = "blue") + geom_point(x = 2020, y = 19606, col = "pink") + scale_y_continuous(limits = c(0, 90000)) + labs(title = "Notifications Time Series for Males 5-14 yrs")

###Trial at new plot 
only19_m514 <- perc_world %>% filter(year == 2019) %>% select(g_whoregion, year, newrel_m514)
colnames(only19_m514) <- c("g_whoregion", "year", "value")
onlypr_m514 <- pivot_longer(bound_m514, cols = c("AFR", "AMR", "EMR", "EUR", "SEA", "WPR"))
onlypr_m514 <- onlypr_m514[,c(2,1,3)]
onlypr_m514$Year <- as.numeric(onlypr_m514$Year)
colnames(onlypr_m514) <- c("g_whoregion", "year", "value")
pred19_m514 <- rbind(only19_m514, onlypr_m514)

#filter continent  
pullreg_m514 <- function(region){
  x <- pred19_m514 %>% filter(g_whoregion == region)
  return(x)
}

###frame for real data from 2019 to 2020 for second line segment 
incl20_m514 <- perc_world %>% filter(year == c(2019, 2020)) %>% select(g_whoregion, year, newrel_m514)
colnames(incl20_m514) <- c("g_whoregion", "year", "value")

pullreal_m514 <- function(region){
  x <- incl20_m514 %>% filter(g_whoregion == region)
  return(x)}

####New plot 
test_males514 <- ggplot() + geom_line(data = males_514, aes(x = year, y = newrel_m514, col = g_whoregion)) + geom_line(data = pullreg_m514("AFR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_m514("AMR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_m514("EMR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_m514("EUR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_m514("SEA"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_m514("WPR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreal_m514("AFR"), aes(x = year, y = value)) + geom_line(data = pullreal_m514("AMR"), aes(x = year, y = value)) + geom_line(data = pullreal_m514("EMR"), aes(x = year, y = value)) + geom_line(data = pullreal_m514("EUR"), aes(x = year, y = value)) + geom_line(data = pullreal_m514("SEA"), aes(x = year, y = value)) + geom_line(data = pullreal_m514("WPR"), aes(x = year, y = value))

#females514
females_514 <- select(perc_world, c(g_whoregion, year, newrel_f514)) 
females_514 <- mutate(females_514, newrel_f514 = ifelse(year == "2020", NA, newrel_f514))
gg_females514 <- ggplot(females_514, aes(x = year, y = newrel_f514, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 32411, col = "red") + geom_point(x = 2020, y = 2897, col = "brown") + geom_point(x = 2020, y = 21632, col = "green") + geom_point(x = 2020, y = 2953, col = "turquoise2") + geom_point(x = 2020, y = 87086, col = "blue") + geom_point(x = 2020, y = 17500, col = "pink") + scale_y_continuous(limits = c(0, 100000)) + labs(title = "Notifications Time Series for Females 5-14 yrs")

###Trial at new plot 
only19_f514 <- perc_world %>% filter(year == 2019) %>% select(g_whoregion, year, newrel_f514)
colnames(only19_f514) <- c("g_whoregion", "year", "value")
onlypr_f514 <- pivot_longer(bound_f514, cols = c("AFR", "AMR", "EMR", "EUR", "SEA", "WPR"))
onlypr_f514 <- onlypr_f514[,c(2,1,3)]
onlypr_f514$Year <- as.numeric(onlypr_f514$Year)
colnames(onlypr_f514) <- c("g_whoregion", "year", "value")
pred19_f514 <- rbind(only19_f514, onlypr_f514)

#filter continent  
pullreg_f514 <- function(region){
  x <- pred19_f514 %>% filter(g_whoregion == region)
  return(x)
}

###frame for real data from 2019 to 2020 for second line segment 
incl20_f514 <- perc_world %>% filter(year == c(2019, 2020)) %>% select(g_whoregion, year, newrel_f514)
colnames(incl20_f514) <- c("g_whoregion", "year", "value")

pullreal_f514 <- function(region){
  x <- incl20_f514 %>% filter(g_whoregion == region)
  return(x)}

####New plot 
test_females514 <- ggplot() + geom_line(data = females_514, aes(x = year, y = newrel_f514, col = g_whoregion)) + geom_line(data = pullreg_f514("AFR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_f514("AMR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_f514("EMR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_f514("EUR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_f514("SEA"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_f514("WPR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreal_f514("AFR"), aes(x = year, y = value)) + geom_line(data = pullreal_f514("AMR"), aes(x = year, y = value)) + geom_line(data = pullreal_f514("EMR"), aes(x = year, y = value)) + geom_line(data = pullreal_f514("EUR"), aes(x = year, y = value)) + geom_line(data = pullreal_f514("SEA"), aes(x = year, y = value)) + geom_line(data = pullreal_f514("WPR"), aes(x = year, y = value))

#males 15 plus plot 
males_15plus <- select(perc_world, c(g_whoregion, year, newrel_m15plus)) 
males_15plus <- mutate(males_15plus, newrel_m15plus = ifelse(year == "2020", NA, newrel_m15plus))
gg_males15plus <- ggplot(males_15plus, aes(x = year, y = newrel_m15plus, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 661953, col = "red") + geom_point(x = 2020, y = 154305.3, col = "brown") + geom_point(x = 2020, y = 226294, col = "green") + geom_point(x = 2020, y = 129819.7, col = "turquoise2") + geom_point(x = 2020, y = 1931341, col = "blue") + geom_point(x = 2020, y = 891849, col = "pink") + scale_y_continuous(limits = c(0, 2000000)) + labs(title = "Notifications Time Series for Males 15plus yrs")

###Trial at new plot 
only19_m15plus <- perc_world %>% filter(year == 2019) %>% select(g_whoregion, year, newrel_m15plus)
colnames(only19_m15plus) <- c("g_whoregion", "year", "value")
onlypr_m15plus <- pivot_longer(bound_m15plus, cols = c("AFR", "AMR", "EMR", "EUR", "SEA", "WPR"))
onlypr_m15plus <- onlypr_m15plus[,c(2,1,3)]
onlypr_m15plus$Year <- as.numeric(onlypr_m15plus$Year)
colnames(onlypr_m15plus) <- c("g_whoregion", "year", "value")
pred19_m15plus <- rbind(only19_m15plus, onlypr_m15plus)

#filter continent  
pullreg_m15plus <- function(region){
  x <- pred19_m15plus %>% filter(g_whoregion == region)
  return(x)
}

###frame for real data from 2019 to 2020 for second line segment 
incl20_m15plus <- perc_world %>% filter(year == c(2019, 2020)) %>% select(g_whoregion, year, newrel_m15plus)
colnames(incl20_m15plus) <- c("g_whoregion", "year", "value")

pullreal_m15plus <- function(region){
  x <- incl20_m15plus %>% filter(g_whoregion == region)
  return(x)}

####New plot 
test_males15plus <- ggplot() + geom_line(data = males_15plus, aes(x = year, y = newrel_m15plus, col = g_whoregion)) + geom_line(data = pullreg_m15plus("AFR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_m15plus("AMR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_m15plus("EMR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_m15plus("EUR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_m15plus("SEA"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_m15plus("WPR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreal_m15plus("AFR"), aes(x = year, y = value)) + geom_line(data = pullreal_m15plus("AMR"), aes(x = year, y = value)) + geom_line(data = pullreal_m15plus("EMR"), aes(x = year, y = value)) + geom_line(data = pullreal_m15plus("EUR"), aes(x = year, y = value)) + geom_line(data = pullreal_m15plus("SEA"), aes(x = year, y = value)) + geom_line(data = pullreal_m15plus("WPR"), aes(x = year, y = value))


#females 15 plus plot 
females_15plus <- select(perc_world, c(g_whoregion, year, newrel_f15plus)) 
females_15plus <- mutate(females_15plus, newrel_f15plus = ifelse(year == "2020", NA, newrel_f15plus))
gg_females15plus <- ggplot(females_15plus, aes(x = year, y = newrel_f15plus, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 426790, col = "red") + geom_point(x = 2020, y = 77417, col = "brown") + geom_point(x = 2020, y = 201386, col = "green") + geom_point(x = 2020, y = 66222.5, col = "turquoise2") + geom_point(x = 2020, y = 1182645, col = "blue") + geom_point(x = 2020, y = 435651, col = "pink") + scale_y_continuous(limits = c(0, 1200000)) + labs(title = "Notifications Time Series for Females 15plus yrs")

###Trial at new plot 
only19_f15plus <- perc_world %>% filter(year == 2019) %>% select(g_whoregion, year, newrel_f15plus)
colnames(only19_f15plus) <- c("g_whoregion", "year", "value")
onlypr_f15plus <- pivot_longer(bound_f15plus, cols = c("AFR", "AMR", "EMR", "EUR", "SEA", "WPR"))
onlypr_f15plus <- onlypr_f15plus[,c(2,1,3)]
onlypr_f15plus$Year <- as.numeric(onlypr_f15plus$Year)
colnames(onlypr_f15plus) <- c("g_whoregion", "year", "value")
pred19_f15plus <- rbind(only19_f15plus, onlypr_f15plus)

#filter continent  
pullreg_f15plus <- function(region){
  x <- pred19_f15plus %>% filter(g_whoregion == region)
  return(x)
}

###frame for real data from 2019 to 2020 for second line segment 
incl20_f15plus <- perc_world %>% filter(year == c(2019, 2020)) %>% select(g_whoregion, year, newrel_f15plus)
colnames(incl20_f15plus) <- c("g_whoregion", "year", "value")

pullreal_f15plus <- function(region){
  x <- incl20_f15plus %>% filter(g_whoregion == region)
  return(x)}

####New plot 
test_females15plus <- ggplot() + geom_line(data = females_15plus, aes(x = year, y = newrel_f15plus, col = g_whoregion)) + geom_line(data = pullreg_f15plus("AFR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_f15plus("AMR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_f15plus("EMR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_f15plus("EUR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_f15plus("SEA"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreg_f15plus("WPR"), aes(x = year, y = value), linetype = "dotted") + geom_line(data = pullreal_f15plus("AFR"), aes(x = year, y = value)) + geom_line(data = pullreal_f15plus("AMR"), aes(x = year, y = value)) + geom_line(data = pullreal_f15plus("EMR"), aes(x = year, y = value)) + geom_line(data = pullreal_f15plus("EUR"), aes(x = year, y = value)) + geom_line(data = pullreal_f15plus("SEA"), aes(x = year, y = value)) + geom_line(data = pullreal_f15plus("WPR"), aes(x = year, y = value))

#create all tables 
test_males04
test_females04
test_males514
test_females514
test_males15plus
test_females15plus


```
Creating notification graphs for entire world 

```{r}
#Putting together notification data for years 2013 to 2020
world_data <- world_2013[,-c(1:3)] %>% group_by(year) %>% na.omit()
tot_m04 <- summarise(world_data, total = sum(newrel_m04))
tot_m04 %<>% as.data.frame()
colnames(tot_m04) <- c("year", "m04")

tot_f04 <- summarise(world_data, total = sum(newrel_f04))
tot_f04 %<>% as.data.frame()
colnames(tot_f04) <- c("year", "f04")

tot_m514 <- summarise(world_data, total = sum(newrel_m514))
tot_m514 %<>% as.data.frame()
colnames(tot_m514) <- c("year", "m514")

tot_f514 <- summarise(world_data, total = sum(newrel_f514))
tot_f514 %<>% as.data.frame()
colnames(tot_f514) <- c("year", "f514")

tot_m15plus <- summarise(world_data, total = sum(newrel_m15plus))
tot_m15plus %<>% as.data.frame()
colnames(tot_m15plus) <- c("year", "m15plus")

tot_f15plus <- summarise(world_data, total = sum(newrel_f15plus))
tot_f15plus %<>% as.data.frame()
colnames(tot_f15plus) <- c("year", "f15plus")

all_tots <- cbind(tot_m04, tot_f04$f04, tot_m514$m514, tot_f514$f514, tot_m15plus$m15plus, tot_f15plus$f15plus)
colnames(all_tots) <- c("year", "case_m04", "case_f04", "case_m514", "case_f514", "case_m15plus", "case_f15plus")

all_tots <- mutate(all_tots, "all04" = case_m04 + case_f04, "all514" = case_m514 + case_f514, "all15plus" = case_m15plus + case_f15plus, "all_male" = case_m04 + case_m514 + case_m15plus, "all_female" = case_f04 + case_f514 + case_f15plus, "all_cases" = all_male + all_female)

all_copy <- all_tots
all_copy04 <- all_copy[, c(1:3)] 
colnames(all_copy04) <- c("year", "male", "female")
all_copy04 <- pivot_longer(all_copy04, cols = c(male, female), names_to = "sex", values_to = "cases")

all_copy514 <- all_copy[, c(1,4,5)] 
colnames(all_copy514) <- c("year", "male", "female")
all_copy514 <- pivot_longer(all_copy514, cols = c(male, female), names_to = "sex", values_to = "cases")

all_copy15plus <- all_copy[, c(1,6,7)] 
colnames(all_copy15plus) <- c("year", "male", "female")
all_copy15plus <- pivot_longer(all_copy15plus, cols = c(male, female), names_to = "sex", values_to = "cases")

all_copy15plus <- all_copy[, c(1,6,7)] 
colnames(all_copy15plus) <- c("year", "male", "female")
all_copy15plus <- pivot_longer(all_copy15plus, cols = c(male, female), names_to = "sex", values_to = "cases")

all_copyall <- all_copy[, c(1,11,12)] 
colnames(all_copyall) <- c("year", "male", "female")
all_copyall <- pivot_longer(all_copyall, cols = c(male, female), names_to = "sex", values_to = "cases")

###Drawing the plots 
world_04 <- ggplot(all_copy04, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst 0-4 yrs from 2013 to 2020")

world_514 <- ggplot(all_copy514, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst 5-14 yrs from 2013 to 2020")

world_15plus <- ggplot(all_copy15plus, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst 15plus yrs from 2013 to 2020")

world_all <- ggplot(all_copyall, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst All Ages from 2013 to 2020")

```

Repeating the continental difference between actual and predicted and including column for overall 

```{r}
#extracting data for whole world 
summed_m04 <- tot_world %>% group_by(year) %>% na.omit()
summed_m04 <- summarise(summed_m04, total = sum(newrel_m04))
colnames(summed_m04) <- c("year", "m04")

summed_f04 <- tot_world %>% group_by(year) %>% na.omit()
summed_f04 <- summarise(summed_f04, total = sum(newrel_f04))
colnames(summed_f04) <- c("year", "f04")

summed_m514 <- tot_world %>% group_by(year) %>% na.omit()
summed_m514 <- summarise(summed_m514, total = sum(newrel_m514))
colnames(summed_m514) <- c("year", "m514")

summed_f514 <- tot_world %>% group_by(year) %>% na.omit()
summed_f514 <- summarise(summed_f514, total = sum(newrel_f514))
colnames(summed_f514) <- c("year", "f514")

summed_m15plus <- tot_world %>% group_by(year) %>% na.omit()
summed_m15plus <- summarise(summed_m15plus, total = sum(newrel_m15plus))
colnames(summed_m15plus) <- c("year", "m15plus")

summed_f15plus <- tot_world %>% group_by(year) %>% na.omit()
summed_f15plus <- summarise(summed_f15plus, total = sum(newrel_f15plus))
colnames(summed_f15plus) <- c("year", "f15plus")

all_summed <- cbind(summed_m04, summed_f04$f04, summed_m514$m514, summed_f514$f514, summed_m15plus$m15plus, summed_f15plus$f15plus)
colnames(all_summed) <- c("year", "m04", "f04", "m514", "f514", "m15plus", "f15plus")

##Attempt at creating forecast 
arima_allm04 <- all_summed[-8, 2]
ts_arimam04 <- ts(arima_allm04, start = 2013, frequency = 1)
ts_arimam04 %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_m04 <- forecast(ts_arimam04, h = 1)
autoplot(forecast_m04)
sum_m04 <- print(summary(forecast_m04))

arima_allf04 <- all_summed[-8, 3]
ts_arimaf04 <- ts(arima_allf04, start = 2013, frequency = 1)
ts_arimaf04 %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_f04 <- forecast(ts_arimaf04, h = 1)
autoplot(forecast_f04)
sum_f04 <- print(summary(forecast_f04))

arima_allm514 <- all_summed[-8, 4]
ts_arimam514 <- ts(arima_allm514, start = 2013, frequency = 1)
ts_arimam514 %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_m514 <- forecast(ts_arimam514, h = 1)
autoplot(forecast_m514)
sum_m514 <- print(summary(forecast_m514))

arima_allf514 <- all_summed[-8, 5]
ts_arimaf514 <- ts(arima_allf514, start = 2013, frequency = 1)
ts_arimaf514 %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_f514 <- forecast(ts_arimaf514, h = 1)
autoplot(forecast_f514)
sum_f514 <- print(summary(forecast_f514))

arima_allm15plus <- all_summed[-8, 6]
ts_arimam15plus <- ts(arima_allm15plus, start = 2013, frequency = 1)
ts_arimam15plus %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_m15plus <- forecast(ts_arimam15plus, h = 1)
autoplot(forecast_m15plus)
sum_m15plus <- print(summary(forecast_m15plus))

arima_allf15plus <- all_summed[-8, 7]
ts_arimaf15plus <- ts(arima_allf15plus, start = 2013, frequency = 1)
ts_arimaf15plus %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_f15plus <- forecast(ts_arimaf15plus, h = 1)
autoplot(forecast_f15plus)
sum_f15plus <- print(summary(forecast_f15plus))

##Creating single data frame with best estimates for each group 
best_estimates <- cbind(sum_m04$`Point Forecast`, sum_f04$`Point Forecast`, sum_m514$`Point Forecast`, sum_f514$`Point Forecast`, sum_m15plus$`Point Forecast`, sum_f15plus$`Point Forecast`)
colnames(best_estimates) <- c("m04", "f04", "m514", "f514", "m15plus", "f15plus")

all_compared <- rbind(all_summed[8,-1], best_estimates)
rownames(all_compared) <- c("Actual", "Predicted")

all_differences <- t(all_compared)
all_differences %<>% as.data.frame()
all_differences <- mutate(all_differences, Percentage = 100*((Actual-Predicted)/Predicted))


sex <- c("m", "f")
add_col1 <- cbind(all_differences, sex)
age_group <- c("04", "04", "514", "514", "15plus", "15plus")
add_col2 <- cbind(add_col1, age_group)
g_whoregion <- "ALL"
add_col3 <- cbind(add_col2, g_whoregion)

#Ready to merge 
ready_all <- add_col3[, c(5,4,6,3)]
colnames(ready_all)[colnames(ready_all) == "Percentage"] <- "value"

piv_four <- rbind(piv_three, ready_all)
piv_four <- piv_four %>% arrange(age_group) %>% group_by(age_group) %>% arrange(sex)
piv_four$g_whoregion <- factor(piv_four$g_whoregion, levels = c("AFR", "AMR", "EMR", "EUR", "SEA", "WPR", "ALL"))

arima_four <- ggplot(piv_four, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_four$age_group) + geom_bar(stat = "identity", position = "dodge") + theme(axis.text.x = element_text(angle = 90)) + theme(legend.title = element_blank()) + xlab("Region") + ylab("Percentage Difference Between Actual and Predicted Cases in 2020") + labs(title = "Difference Between Actual and Predicted Notifications in 2020")

arima_four 
```
